{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"11Naive Bayes.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"cells":[{"metadata":{"id":"7iKiWQqif_5f","colab_type":"code","colab":{}},"cell_type":"code","source":["%%cmd\n","pip install watermark"],"execution_count":0,"outputs":[]},{"metadata":{"id":"D_tZwiabf_5h","colab_type":"code","colab":{}},"cell_type":"code","source":["#在这里必须分开cell，魔法命令必须是一个cell的第一部分内容\n","#注意load_ext这个命令只能够执行一次，再执行就会报错，要求用reload命令"],"execution_count":0,"outputs":[]},{"metadata":{"id":"6W7xDDQzgPyw","colab_type":"code","outputId":"3f616da4-d849-49cc-92f1-9ff5f269c881","executionInfo":{"status":"ok","timestamp":1547538867202,"user_tz":-480,"elapsed":6013,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":306}},"cell_type":"code","source":["!pip install watermark"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Collecting watermark\n","  Downloading https://files.pythonhosted.org/packages/6b/35/03c08e8cd5ec4fc786fe23180bbb34a9b206106605669bbf007410cb6e8d/watermark-1.8.0-py3-none-any.whl\n","Requirement already satisfied: ipython in /usr/local/lib/python3.6/dist-packages (from watermark) (5.5.0)\n","Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (4.3.2)\n","Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (0.8.1)\n","Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (40.6.3)\n","Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (4.3.0)\n","Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (0.7.5)\n","Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (4.6.0)\n","Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (2.1.3)\n","Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython->watermark) (1.0.15)\n","Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->watermark) (0.2.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->watermark) (1.11.0)\n","Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->watermark) (0.6.0)\n","Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->watermark) (0.1.7)\n","Installing collected packages: watermark\n","Successfully installed watermark-1.8.0\n"],"name":"stdout"}]},{"metadata":{"id":"-952fvZZf_5k","colab_type":"code","colab":{}},"cell_type":"code","source":["%load_ext watermark"],"execution_count":0,"outputs":[]},{"metadata":{"id":"XL2wEDC4f_5m","colab_type":"code","outputId":"13f287b5-bd82-4d05-df4e-5ed2fd387d56","executionInfo":{"status":"ok","timestamp":1547538881821,"user_tz":-480,"elapsed":795,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":323}},"cell_type":"code","source":["%watermark -a \"ys\" -d -v -m -p numpy,pandas,matplotlib,scipy,sklearn"],"execution_count":0,"outputs":[{"output_type":"stream","text":["ys 2019-01-15 \n","\n","CPython 3.6.7\n","IPython 5.5.0\n","\n","numpy 1.14.6\n","pandas 0.22.0\n","matplotlib 2.1.2\n","scipy 1.1.0\n","sklearn 0.20.2\n","\n","compiler   : GCC 8.2.0\n","system     : Linux\n","release    : 4.14.79+\n","machine    : x86_64\n","processor  : x86_64\n","CPU cores  : 2\n","interpreter: 64bit\n"],"name":"stdout"}]},{"metadata":{"id":"SgINGD3IgdcB","colab_type":"text"},"cell_type":"markdown","source":["# 高斯朴素贝叶斯GaussianNB"]},{"metadata":{"id":"unLnRNEzf_5r","colab_type":"code","colab":{}},"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn.naive_bayes import GaussianNB\n","from sklearn.datasets import load_digits\n","from sklearn.model_selection import train_test_split"],"execution_count":0,"outputs":[]},{"metadata":{"id":"lbFa7XPaf_5t","colab_type":"code","colab":{}},"cell_type":"code","source":["digits = load_digits() #手写数据集\n","X, y = digits.data, digits.target"],"execution_count":0,"outputs":[]},{"metadata":{"id":"yaH5H8KTf_5v","colab_type":"code","colab":{}},"cell_type":"code","source":["Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,y,test_size=0.3,random_state=420)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"7_4Lz59pf_5x","colab_type":"code","outputId":"228243b1-849c-43b3-ee5a-11c8a097cab8","executionInfo":{"status":"ok","timestamp":1547538933337,"user_tz":-480,"elapsed":816,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["Xtrain.shape #64个特征"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1257, 64)"]},"metadata":{"tags":[]},"execution_count":8}]},{"metadata":{"id":"BTb7HSiJf_50","colab_type":"code","outputId":"42b0dd2b-9fae-4b90-f62b-8b8ef952bea9","executionInfo":{"status":"ok","timestamp":1547538936787,"user_tz":-480,"elapsed":2317,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["Xtest.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(540, 64)"]},"metadata":{"tags":[]},"execution_count":9}]},{"metadata":{"id":"ZpnF_Uknf_52","colab_type":"code","outputId":"93f719f3-fc7e-4ff9-8e3f-a0244d0017ed","executionInfo":{"status":"ok","timestamp":1547538939978,"user_tz":-480,"elapsed":1585,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["np.unique(Ytrain) #多分类问题，类别是10个"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"]},"metadata":{"tags":[]},"execution_count":10}]},{"metadata":{"id":"gK9qB_J2f_56","colab_type":"code","colab":{}},"cell_type":"code","source":["gnb = GaussianNB().fit(Xtrain,Ytrain)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"DY7ZCxBDf_57","colab_type":"code","colab":{}},"cell_type":"code","source":["#查看分数\n","acc_score = gnb.score(Xtest,Ytest) #返回预测的精确性 accuracy"],"execution_count":0,"outputs":[]},{"metadata":{"id":"o6KGjjKHf_5-","colab_type":"code","outputId":"88349b88-9f20-43b0-8dcd-061432d941c3","executionInfo":{"status":"ok","timestamp":1547538950758,"user_tz":-480,"elapsed":1090,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["acc_score"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8592592592592593"]},"metadata":{"tags":[]},"execution_count":13}]},{"metadata":{"id":"3NEijjU1f_6C","colab_type":"code","colab":{}},"cell_type":"code","source":["#查看预测结果\n","Y_pred = gnb.predict(Xtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"HhZpk-4Of_6E","colab_type":"code","outputId":"a3ae91af-c4a6-4d44-f65e-c07b0a1840ee","executionInfo":{"status":"ok","timestamp":1547538957016,"user_tz":-480,"elapsed":832,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":442}},"cell_type":"code","source":["Y_pred"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([6, 1, 3, 0, 4, 5, 0, 8, 3, 8, 6, 8, 7, 8, 8, 8, 5, 9, 5, 6, 5, 4,\n","       7, 4, 8, 2, 7, 2, 8, 9, 2, 8, 3, 6, 0, 3, 8, 8, 1, 5, 2, 8, 8, 9,\n","       2, 2, 0, 7, 3, 6, 7, 2, 8, 0, 5, 4, 1, 9, 4, 0, 5, 8, 9, 1, 7, 8,\n","       7, 5, 8, 2, 4, 4, 8, 2, 6, 1, 2, 1, 7, 8, 8, 5, 9, 4, 3, 6, 9, 7,\n","       4, 2, 4, 8, 0, 5, 7, 7, 7, 4, 7, 8, 8, 7, 0, 7, 2, 1, 9, 9, 8, 7,\n","       1, 5, 1, 8, 0, 4, 8, 9, 5, 6, 4, 8, 3, 8, 0, 6, 8, 6, 7, 6, 1, 8,\n","       5, 0, 8, 2, 1, 8, 8, 6, 6, 0, 2, 4, 7, 8, 9, 5, 9, 4, 7, 8, 8, 6,\n","       7, 0, 8, 4, 7, 2, 2, 6, 4, 4, 1, 0, 3, 4, 3, 8, 7, 0, 6, 9, 7, 5,\n","       5, 3, 6, 1, 6, 6, 2, 3, 8, 2, 7, 3, 1, 1, 6, 8, 8, 8, 7, 7, 2, 5,\n","       0, 0, 8, 6, 6, 7, 6, 0, 7, 5, 5, 8, 4, 6, 5, 1, 5, 1, 9, 6, 8, 8,\n","       8, 2, 4, 8, 6, 5, 9, 9, 3, 1, 9, 1, 3, 3, 5, 5, 7, 7, 4, 0, 9, 0,\n","       9, 9, 6, 4, 3, 4, 8, 1, 0, 2, 9, 7, 6, 8, 8, 0, 6, 0, 1, 7, 1, 9,\n","       5, 4, 6, 8, 1, 5, 7, 7, 5, 1, 0, 0, 9, 3, 9, 1, 6, 3, 7, 2, 7, 1,\n","       9, 9, 8, 3, 3, 5, 7, 7, 7, 3, 9, 5, 0, 7, 5, 5, 1, 4, 9, 2, 0, 6,\n","       3, 0, 8, 7, 2, 8, 1, 6, 4, 1, 2, 5, 7, 1, 4, 9, 5, 4, 2, 3, 5, 9,\n","       8, 0, 0, 0, 0, 4, 2, 0, 6, 6, 8, 7, 1, 1, 8, 1, 1, 7, 8, 7, 8, 3,\n","       1, 4, 6, 1, 8, 1, 6, 6, 7, 2, 8, 5, 3, 2, 1, 8, 7, 8, 5, 1, 7, 2,\n","       1, 1, 7, 8, 9, 5, 0, 4, 7, 8, 8, 9, 5, 5, 8, 5, 5, 8, 1, 0, 4, 3,\n","       8, 2, 8, 5, 7, 6, 9, 9, 5, 8, 9, 9, 1, 8, 6, 4, 3, 3, 3, 3, 0, 8,\n","       0, 7, 7, 6, 0, 8, 9, 8, 3, 6, 6, 8, 7, 5, 8, 4, 5, 8, 6, 7, 6, 7,\n","       7, 8, 0, 8, 2, 2, 0, 5, 7, 3, 0, 2, 8, 2, 0, 2, 3, 6, 8, 1, 7, 5,\n","       7, 1, 7, 7, 2, 7, 5, 2, 6, 5, 8, 0, 0, 8, 1, 3, 7, 6, 1, 5, 6, 2,\n","       0, 1, 5, 7, 8, 0, 3, 5, 0, 7, 5, 4, 4, 1, 5, 9, 5, 3, 7, 1, 7, 3,\n","       5, 8, 5, 8, 5, 6, 1, 6, 7, 4, 3, 7, 0, 5, 4, 9, 3, 3, 6, 3, 5, 2,\n","       9, 8, 9, 3, 9, 7, 3, 4, 9, 4, 3, 1])"]},"metadata":{"tags":[]},"execution_count":15}]},{"metadata":{"id":"lb4QITA-f_6H","colab_type":"code","colab":{}},"cell_type":"code","source":["#查看预测的概率结果\n","prob = gnb.predict_proba(Xtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"DDN4nbpOf_6K","colab_type":"code","outputId":"b29f16d4-31fe-459a-b9c3-a3a5ff710fdc","executionInfo":{"status":"ok","timestamp":1547538964620,"user_tz":-480,"elapsed":852,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":238}},"cell_type":"code","source":["prob"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.00000000e+000, 4.69391744e-052, 1.74871280e-098, ...,\n","        0.00000000e+000, 4.19588993e-033, 1.51751459e-119],\n","       [0.00000000e+000, 1.00000000e+000, 9.26742456e-013, ...,\n","        0.00000000e+000, 0.00000000e+000, 0.00000000e+000],\n","       [0.00000000e+000, 0.00000000e+000, 3.73608152e-026, ...,\n","        0.00000000e+000, 1.29541754e-039, 5.54684869e-077],\n","       ...,\n","       [0.00000000e+000, 2.43314963e-047, 4.82483668e-305, ...,\n","        2.31612692e-008, 1.23891596e-126, 2.87896140e-257],\n","       [0.00000000e+000, 8.26462929e-129, 4.99150558e-012, ...,\n","        0.00000000e+000, 4.01802372e-003, 6.19000712e-013],\n","       [0.00000000e+000, 9.99929965e-001, 1.45462767e-013, ...,\n","        5.05856094e-005, 1.94498169e-005, 3.42317317e-042]])"]},"metadata":{"tags":[]},"execution_count":17}]},{"metadata":{"id":"ZJvLp-Ayf_6N","colab_type":"code","outputId":"55381496-b5ec-4ea9-e1c0-29dc37f9c259","executionInfo":{"status":"ok","timestamp":1547538968046,"user_tz":-480,"elapsed":869,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["prob.shape #每一列对应一个标签类别下的概率"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(540, 10)"]},"metadata":{"tags":[]},"execution_count":18}]},{"metadata":{"id":"WdffAVsyf_6Q","colab_type":"code","outputId":"d2603710-05c2-44df-fa5f-0bc0f0a7235d","executionInfo":{"status":"ok","timestamp":1547538970524,"user_tz":-480,"elapsed":834,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["prob[1,:].sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.000000000000003"]},"metadata":{"tags":[]},"execution_count":19}]},{"metadata":{"id":"j0nWHztff_6U","colab_type":"code","outputId":"69f30f12-e924-4d24-ef84-fd18f3f948a3","executionInfo":{"status":"ok","timestamp":1547538984336,"user_tz":-480,"elapsed":936,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["prob[3,:].sum() #每一行的和都是一"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.0"]},"metadata":{"tags":[]},"execution_count":20}]},{"metadata":{"id":"j0khR1LIf_6W","colab_type":"code","outputId":"4bea1aea-348f-4e17-8d6a-009ad90a0f6b","executionInfo":{"status":"ok","timestamp":1547538991250,"user_tz":-480,"elapsed":3146,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":561}},"cell_type":"code","source":["prob.sum(axis=1)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n","       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"]},"metadata":{"tags":[]},"execution_count":21}]},{"metadata":{"id":"Gru7FLX8g1ES","colab_type":"text"},"cell_type":"markdown","source":["## 使用混淆矩阵来查看贝叶斯的分类结果"]},{"metadata":{"id":"AL0MXGH6f_6Y","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.metrics import confusion_matrix as CM"],"execution_count":0,"outputs":[]},{"metadata":{"id":"5UqCyZjAf_6b","colab_type":"code","outputId":"30b69de4-7a15-47fc-a43e-74bd44c6b76f","executionInfo":{"status":"ok","timestamp":1547539020107,"user_tz":-480,"elapsed":844,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"cell_type":"code","source":["CM(Ytest,Y_pred)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[47,  0,  0,  0,  0,  0,  0,  1,  0,  0],\n","       [ 0, 46,  2,  0,  0,  0,  0,  3,  6,  2],\n","       [ 0,  2, 35,  0,  0,  0,  1,  0, 16,  0],\n","       [ 0,  0,  1, 40,  0,  1,  0,  3,  4,  0],\n","       [ 0,  0,  1,  0, 39,  0,  1,  4,  0,  0],\n","       [ 0,  0,  0,  2,  0, 58,  1,  1,  1,  0],\n","       [ 0,  0,  1,  0,  0,  1, 49,  0,  0,  0],\n","       [ 0,  0,  0,  0,  0,  0,  0, 54,  0,  0],\n","       [ 0,  3,  0,  1,  0,  0,  0,  2, 55,  0],\n","       [ 1,  1,  0,  1,  2,  0,  0,  3,  7, 41]])"]},"metadata":{"tags":[]},"execution_count":23}]},{"metadata":{"id":"VdEd1H7Rg6i2","colab_type":"text"},"cell_type":"markdown","source":["##  探索贝叶斯：高斯朴素贝叶斯擅长的数据集"]},{"metadata":{"id":"aB77Svm0hd1J","colab_type":"text"},"cell_type":"markdown","source":["##  探索贝叶斯：高斯朴素贝叶斯的拟合效果与运算速度"]},{"metadata":{"id":"sAC4V_8qf_6d","colab_type":"code","colab":{}},"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn.naive_bayes import GaussianNB\n","from sklearn.svm import SVC\n","from sklearn.ensemble import RandomForestClassifier as RFC\n","from sklearn.tree import DecisionTreeClassifier as DTC\n","from sklearn.linear_model import LogisticRegression as LR\n","from sklearn.datasets import load_digits\n","from sklearn.model_selection import learning_curve #画学习曲线的类\n","from sklearn.model_selection import ShuffleSplit #设定交叉验证模式的类\n","from time import time\n","import datetime"],"execution_count":0,"outputs":[]},{"metadata":{"id":"WJVUkWquf_6f","colab_type":"code","colab":{}},"cell_type":"code","source":["def plot_learning_curve(estimator,title, X, y, \n","                        ax, #选择子图\n","                        ylim=None, #设置纵坐标的取值范围\n","                        cv=None, #交叉验证\n","                        n_jobs=None #设定索要使用的线程\n","                       ):\n","    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y\n","                                                            ,cv=cv,n_jobs=n_jobs)    \n","    ax.set_title(title)\n","    if ylim is not None:\n","        ax.set_ylim(*ylim)\n","    ax.set_xlabel(\"Training examples\")\n","    ax.set_ylabel(\"Score\")\n","    ax.grid() #显示网格作为背景，不是必须\n","    ax.plot(train_sizes, np.mean(train_scores, axis=1), 'o-'\n","            , color=\"r\",label=\"Training score\")\n","    ax.plot(train_sizes, np.mean(test_scores, axis=1), 'o-'\n","            , color=\"g\",label=\"Test score\")\n","    ax.legend(loc=\"best\")\n","    return ax"],"execution_count":0,"outputs":[]},{"metadata":{"id":"QwiP8yxcf_6h","colab_type":"code","colab":{}},"cell_type":"code","source":["digits = load_digits()\n","X, y = digits.data, digits.target"],"execution_count":0,"outputs":[]},{"metadata":{"id":"bHURSmRSf_6l","colab_type":"code","outputId":"02019e6c-f82d-498d-d106-13501797d7d7","executionInfo":{"status":"ok","timestamp":1547539053526,"user_tz":-480,"elapsed":551,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["X.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1797, 64)"]},"metadata":{"tags":[]},"execution_count":27}]},{"metadata":{"id":"Qp8j7JdQf_6n","colab_type":"code","outputId":"067fe045-9efc-4f68-dcfa-837f82c90d8b","executionInfo":{"status":"ok","timestamp":1547539056359,"user_tz":-480,"elapsed":901,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"cell_type":"code","source":["X #是一个稀疏矩阵"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n","       [ 0.,  0.,  0., ..., 10.,  0.,  0.],\n","       [ 0.,  0.,  0., ..., 16.,  9.,  0.],\n","       ...,\n","       [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n","       [ 0.,  0.,  2., ..., 12.,  0.,  0.],\n","       [ 0.,  0., 10., ..., 12.,  1.,  0.]])"]},"metadata":{"tags":[]},"execution_count":28}]},{"metadata":{"id":"jFH508z0f_6q","colab_type":"code","colab":{}},"cell_type":"code","source":["title = [\"Naive Bayes\",\"DecisionTree\",\"SVM, RBF kernel\",\"RandomForest\",\"Logistic\"]\n","model = [GaussianNB(),DTC(),SVC(gamma=0.001)\n","         ,RFC(n_estimators=50),LR(C=.1,solver=\"lbfgs\")]\n","cv = ShuffleSplit(n_splits=50, test_size=0.2, random_state=0)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"03w16_7Xf_6s","colab_type":"code","outputId":"8cdb4c07-ca4f-4a2a-e6f3-bde85ba0b31a","executionInfo":{"status":"ok","timestamp":1547539248467,"user_tz":-480,"elapsed":182823,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":491}},"cell_type":"code","source":["fig, axes = plt.subplots(1,5,figsize=(30,6))\n","for ind, title_, estimator in zip(range(len(title)),title,model):\n","    times = time()\n","    plot_learning_curve(estimator, title_, X, y,\n","                        ax=axes[ind], ylim = [0.7, 1.05],n_jobs=4, cv=cv)\n","    print(\"{}:{}\".format(title_,datetime.datetime.fromtimestamp(time()-times).strftime(\"%M:%S:%f\")))\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Naive Bayes:00:05:873966\n","DecisionTree:00:03:431870\n","RandomForest:00:24:234845\n","Logistic:02:02:947937\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABsEAAAGCCAYAAACxTG2pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNXL8tPe3hx0OFcW2l6tLXOmKMSM6Wv6zZyp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at 0x7f4120d3a7b8>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"dSVEzQ29f_6v","colab_type":"code","outputId":"720c5add-b0f0-4286-a638-d130027c573f","executionInfo":{"status":"ok","timestamp":1547539351091,"user_tz":-480,"elapsed":859,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":527}},"cell_type":"code","source":["[*zip(range(len(title)),title,model)]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(0, 'Naive Bayes', GaussianNB(priors=None, var_smoothing=1e-09)),\n"," (1,\n","  'DecisionTree',\n","  DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n","              max_features=None, max_leaf_nodes=None,\n","              min_impurity_decrease=0.0, min_impurity_split=None,\n","              min_samples_leaf=1, min_samples_split=2,\n","              min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n","              splitter='best')),\n"," (2,\n","  'SVM, RBF kernel',\n","  SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n","    decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n","    max_iter=-1, probability=False, random_state=None, shrinking=True,\n","    tol=0.001, verbose=False)),\n"," (3,\n","  'RandomForest',\n","  RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n","              max_depth=None, max_features='auto', max_leaf_nodes=None,\n","              min_impurity_decrease=0.0, min_impurity_split=None,\n","              min_samples_leaf=1, min_samples_split=2,\n","              min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=None,\n","              oob_score=False, random_state=None, verbose=0,\n","              warm_start=False)),\n"," (4,\n","  'Logistic',\n","  LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n","            intercept_scaling=1, max_iter=100, multi_class='warn',\n","            n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n","            tol=0.0001, verbose=0, warm_start=False))]"]},"metadata":{"tags":[]},"execution_count":31}]},{"metadata":{"id":"KeHL6fxzf_6x","colab_type":"code","colab":{}},"cell_type":"code","source":["#我输入我的分类器，我们的数据，画图所需要的一系列参数，交叉验证的模式，以及其他可能的参数\n","#一次性帮助我画出所有的学习曲线\n","\n","#找出每个图像所需要的横纵坐标 - learning_curve\n","#设定好用来绘制子图所在的画布，就开始在画布上绘图\n","\n","def plot_learning_curve(estimator,title, X, y, \n","                        ax, #选择子图\n","                        ylim=None, #设置纵坐标的取值范围\n","                        cv=None, #交叉验证\n","                        n_jobs=None #设定索要使用的线程\n","                       ):\n","    \n","    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y\n","                                                            ,cv=cv,n_jobs=n_jobs)    \n","    ax.set_title(title) #设置标题\n","    if ylim is not None:\n","        ax.set_ylim(*ylim)\n","    ax.set_xlabel(\"Training examples\")\n","    ax.set_ylabel(\"Score\")\n","    ax.grid() #显示网格作为背景，不是必须\n","    ax.plot(train_sizes, np.mean(train_scores, axis=1), 'o-'\n","            , color=\"r\",label=\"Training score\")\n","    ax.plot(train_sizes, np.mean(test_scores, axis=1), 'o-'\n","            , color=\"g\",label=\"Test score\")\n","    ax.legend(loc=\"best\")\n","    return ax"],"execution_count":0,"outputs":[]},{"metadata":{"id":"7sx1eFubf_60","colab_type":"code","colab":{}},"cell_type":"code","source":["clf = GaussianNB()\n","cv = ShuffleSplit(n_splits=50 #把数据分为多少份\n","                  ,test_size=0.2 #20% * 50 份的数据会被作为测试集\n","                  ,random_state=0 #分交叉验证的份数的时候进行的随机抽样的模式\n","                 )"],"execution_count":0,"outputs":[]},{"metadata":{"id":"p_f2KeSff_62","colab_type":"code","colab":{}},"cell_type":"code","source":["train_sizes, train_scores, test_scores = learning_curve(clf #分类器，clf\n","                                                        , X, y #特征矩阵和标签\n","                                                        ,cv=cv #表示交叉验证模式\n","                                                        ,n_jobs=4\n","                                                        #每次运行的时候可以允许算法使用多少运算资源\n","                                                       )"],"execution_count":0,"outputs":[]},{"metadata":{"id":"XB55k8ZQf_64","colab_type":"code","outputId":"d9869f78-b270-4fa1-ae2f-e9e1b876ff7c","executionInfo":{"status":"ok","timestamp":1547539400655,"user_tz":-480,"elapsed":832,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["train_sizes #每次分训练集和测试集建模之后，训练集上的样本数量"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 143,  467,  790, 1113, 1437])"]},"metadata":{"tags":[]},"execution_count":35}]},{"metadata":{"id":"0pa-Xov2f_66","colab_type":"code","outputId":"94a1396f-351e-4b6c-f75b-2cba37c3d93b","executionInfo":{"status":"ok","timestamp":1547539405391,"user_tz":-480,"elapsed":934,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":867}},"cell_type":"code","source":["train_scores #训练集上的分数"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.93006993, 0.94405594, 0.95104895, 0.87412587, 0.95804196,\n","        0.99300699, 0.95804196, 0.96503497, 0.95804196, 0.91608392,\n","        0.93006993, 0.97902098, 0.93706294, 0.90909091, 0.93006993,\n","        0.94405594, 0.97902098, 0.95804196, 0.96503497, 0.94405594,\n","        0.95104895, 0.95804196, 0.97202797, 0.9020979 , 0.96503497,\n","        0.9020979 , 0.83216783, 0.96503497, 0.93006993, 0.95804196,\n","        0.86013986, 0.94405594, 1.        , 0.93006993, 0.97202797,\n","        0.94405594, 0.98601399, 0.95804196, 0.97202797, 0.95804196,\n","        0.97902098, 0.97202797, 0.95804196, 0.95804196, 0.8951049 ,\n","        0.83216783, 0.93006993, 0.90909091, 0.95804196, 0.95804196],\n","       [0.90149893, 0.91006424, 0.90578158, 0.86509636, 0.8993576 ,\n","        0.91862955, 0.89721627, 0.90364026, 0.91220557, 0.86937901,\n","        0.82441113, 0.91220557, 0.90792291, 0.88008565, 0.88650964,\n","        0.90792291, 0.8608137 , 0.90364026, 0.88008565, 0.92077088,\n","        0.90149893, 0.87366167, 0.86295503, 0.86295503, 0.89293362,\n","        0.90364026, 0.84582441, 0.88222698, 0.90364026, 0.87366167,\n","        0.86937901, 0.86295503, 0.9143469 , 0.90149893, 0.92505353,\n","        0.9143469 , 0.91220557, 0.90792291, 0.85224839, 0.88865096,\n","        0.92933619, 0.87794433, 0.86723769, 0.88222698, 0.89293362,\n","        0.82012848, 0.88222698, 0.8993576 , 0.93147752, 0.86295503],\n","       [0.87341772, 0.8556962 , 0.9       , 0.87974684, 0.88101266,\n","        0.88860759, 0.89873418, 0.87088608, 0.88734177, 0.8721519 ,\n","        0.82405063, 0.89493671, 0.91139241, 0.86329114, 0.82278481,\n","        0.88481013, 0.85316456, 0.86329114, 0.89113924, 0.88227848,\n","        0.89493671, 0.86202532, 0.83924051, 0.89113924, 0.8721519 ,\n","        0.87974684, 0.82658228, 0.87848101, 0.87721519, 0.86582278,\n","        0.89493671, 0.86835443, 0.87974684, 0.9       , 0.87341772,\n","        0.87848101, 0.87088608, 0.88734177, 0.81392405, 0.84936709,\n","        0.8835443 , 0.87848101, 0.88101266, 0.86329114, 0.87341772,\n","        0.81772152, 0.87594937, 0.87974684, 0.90759494, 0.88227848],\n","       [0.86163522, 0.87780773, 0.8885894 , 0.87241689, 0.86792453,\n","        0.86702606, 0.86792453, 0.86433064, 0.868823  , 0.84995508,\n","        0.86792453, 0.86612758, 0.89937107, 0.86253369, 0.87061995,\n","        0.87601078, 0.85085355, 0.87241689, 0.85354897, 0.85624438,\n","        0.88499551, 0.8589398 , 0.85534591, 0.8589398 , 0.8589398 ,\n","        0.84186882, 0.82030548, 0.89128482, 0.85354897, 0.868823  ,\n","        0.86792453, 0.86073675, 0.85534591, 0.87151842, 0.87780773,\n","        0.88679245, 0.86163522, 0.87061995, 0.84636119, 0.8490566 ,\n","        0.87601078, 0.86343217, 0.8885894 , 0.8490566 , 0.88679245,\n","        0.85354897, 0.86702606, 0.88050314, 0.868823  , 0.86433064],\n","       [0.8559499 , 0.86986778, 0.87543493, 0.86499652, 0.85247042,\n","        0.86430063, 0.84411969, 0.85664579, 0.88378566, 0.84203201,\n","        0.84899095, 0.86290884, 0.88726514, 0.85247042, 0.85873347,\n","        0.86986778, 0.83298539, 0.83646486, 0.84411969, 0.84551148,\n","        0.8677801 , 0.87473904, 0.86082116, 0.84551148, 0.86847599,\n","        0.84690327, 0.84551148, 0.87543493, 0.85386221, 0.8670842 ,\n","        0.85803758, 0.85455811, 0.83228949, 0.88935282, 0.87752262,\n","        0.86986778, 0.85734168, 0.85107864, 0.85873347, 0.86499652,\n","        0.86430063, 0.87195546, 0.88726514, 0.85177453, 0.87891441,\n","        0.86638831, 0.87125957, 0.88448156, 0.86151705, 0.86569241]])"]},"metadata":{"tags":[]},"execution_count":36}]},{"metadata":{"id":"cICJccQbf_69","colab_type":"code","outputId":"cc48d166-5d07-4065-d899-bdeca6fcb523","executionInfo":{"status":"ok","timestamp":1547539416740,"user_tz":-480,"elapsed":931,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["train_scores.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(5, 50)"]},"metadata":{"tags":[]},"execution_count":37}]},{"metadata":{"id":"Tb01xIFpf_6_","colab_type":"code","outputId":"1aa8373d-d5b6-4ee3-a9b1-d4151c597f8f","executionInfo":{"status":"ok","timestamp":1547539419288,"user_tz":-480,"elapsed":914,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["test_scores.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(5, 50)"]},"metadata":{"tags":[]},"execution_count":38}]},{"metadata":{"id":"7-DZQJVaifk5","colab_type":"text"},"cell_type":"markdown","source":["# 概率类模型的评估指标"]},{"metadata":{"id":"n91uN7i3ii3k","colab_type":"text"},"cell_type":"markdown","source":["## 1 布里尔分数Brier Score"]},{"metadata":{"id":"6E_Nof5Ef_7D","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.metrics import brier_score_loss"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Clc5vneSf_7F","colab_type":"code","outputId":"a4318d7c-f77e-4da8-c27b-6eee69442724","executionInfo":{"status":"ok","timestamp":1547539494749,"user_tz":-480,"elapsed":1110,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["prob.shape "],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(540, 10)"]},"metadata":{"tags":[]},"execution_count":40}]},{"metadata":{"id":"j8nO1xlSf_7I","colab_type":"code","outputId":"e6db7796-eb50-44f5-f64e-4ade94bc9d58","executionInfo":{"status":"ok","timestamp":1547539498386,"user_tz":-480,"elapsed":1082,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["#注意，第一个参数是真实标签，第二个参数是预测出的概率值\n","#在二分类情况下，接口predict_proba会返回两列，但SVC的接口decision_function却只会返回一列\n","#要随时注意，使用了怎样的概率分类器，以辨别查找置信度的接口，以及这些接口的结构\n","brier_score_loss(Ytest, prob[:,1], pos_label=1)\n","#我们的pos_label与prob中的索引一致，就可以查看这个类别下的布里尔分数是多少"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.032619662406118764"]},"metadata":{"tags":[]},"execution_count":41}]},{"metadata":{"id":"vVSmevReixeR","colab_type":"text"},"cell_type":"markdown","source":["### 测试"]},{"metadata":{"id":"r95bHHVZjOMF","colab_type":"code","outputId":"0911630b-fa67-4e71-a674-f284155649b2","executionInfo":{"status":"ok","timestamp":1547539661814,"user_tz":-480,"elapsed":923,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["prob[:,1].shape[0]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["540"]},"metadata":{"tags":[]},"execution_count":45}]},{"metadata":{"id":"3CEJsN_Li0Bw","colab_type":"code","outputId":"4374c02d-a89f-4c03-a14c-aa63924da3e6","executionInfo":{"status":"ok","timestamp":1547539672319,"user_tz":-480,"elapsed":1034,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["np.sum((prob[:,1]-1)**2)/prob[:,1].shape[0]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8999929141952214"]},"metadata":{"tags":[]},"execution_count":46}]},{"metadata":{"id":"u3ZkPZttjiWX","colab_type":"text"},"cell_type":"markdown","source":["不是这么简单的计算.."]},{"metadata":{"id":"lWc8n2tyjuF-","colab_type":"text"},"cell_type":"markdown","source":["### 逻辑回归，SVC和我们的高斯朴素贝叶斯的效果"]},{"metadata":{"id":"vSdUdj4tf_7O","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.svm import SVC\n","from sklearn.linear_model import LogisticRegression as LR"],"execution_count":0,"outputs":[]},{"metadata":{"id":"CFX-Ocd4f_7R","colab_type":"code","outputId":"087021af-19db-48a7-a26d-09afc83172dd","executionInfo":{"status":"ok","timestamp":1547539788587,"user_tz":-480,"elapsed":916,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["np.unique(Ytrain)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"]},"metadata":{"tags":[]},"execution_count":48}]},{"metadata":{"id":"O39qeydXf_7S","colab_type":"code","outputId":"04ff09b2-979b-4ad7-820b-d4b804d4ffdc","executionInfo":{"status":"ok","timestamp":1547539792284,"user_tz":-480,"elapsed":905,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["Xtrain.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1257, 64)"]},"metadata":{"tags":[]},"execution_count":49}]},{"metadata":{"id":"XJ9MLP56f_7U","colab_type":"code","colab":{}},"cell_type":"code","source":["logi = LR(C=1., solver='lbfgs',max_iter=3000,multi_class=\"auto\").fit(Xtrain,Ytrain)\n","svc = SVC(kernel = \"linear\",gamma=1).fit(Xtrain,Ytrain)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"wJTY5Suuf_7X","colab_type":"code","outputId":"6c17245e-9706-4b07-a2df-9bf7b9120294","executionInfo":{"status":"ok","timestamp":1547539802123,"user_tz":-480,"elapsed":833,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["brier_score_loss(Ytest,logi.predict_proba(Xtest)[:,1],pos_label=1)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.011422731531754838"]},"metadata":{"tags":[]},"execution_count":51}]},{"metadata":{"id":"TMSnOEctf_7a","colab_type":"code","outputId":"f6bc9c85-0918-4354-8274-f92d9aad8e2d","executionInfo":{"status":"ok","timestamp":1547539804589,"user_tz":-480,"elapsed":828,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":238}},"cell_type":"code","source":["svc.decision_function(Xtest)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 6.04999568,  3.90384836,  2.90874813, ..., -0.25222764,\n","         8.30994867,  0.77613135],\n","       [-0.21449942,  9.28421434,  6.08114168, ...,  0.78220746,\n","         7.09763534,  3.98412779],\n","       [ 0.77940329,  2.9077248 ,  7.08524427, ...,  6.07973767,\n","         8.21109292,  5.10728844],\n","       ...,\n","       [ 4.96967342,  5.01152151, -0.18621393, ...,  8.13760707,\n","         6.13234107,  1.8288879 ],\n","       [ 2.9476444 ,  2.73284495,  6.10781709, ...,  2.99252742,\n","         8.26833873,  7.085248  ],\n","       [-0.245529  ,  9.32817444,  4.99213102, ...,  5.98483642,\n","         7.18223468,  1.87536216]])"]},"metadata":{"tags":[]},"execution_count":52}]},{"metadata":{"id":"UuqNZzvFf_7b","colab_type":"code","colab":{}},"cell_type":"code","source":["#由于SVC的置信度并不是概率，为了可比性，我们需要将SVC的置信度“距离”归一化，压缩到[0,1]之间\n","svc_prob = (svc.decision_function(Xtest) - svc.decision_function(Xtest).min())/(svc.decision_function(Xtest).max() - svc.decision_function(Xtest).min())"],"execution_count":0,"outputs":[]},{"metadata":{"id":"_tLDhmTQf_7d","colab_type":"code","outputId":"55adfbbd-6db2-44f8-f234-7ac3848cbd05","executionInfo":{"status":"ok","timestamp":1547539813403,"user_tz":-480,"elapsed":929,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["brier_score_loss(Ytest,svc_prob[:,1],pos_label=1)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.23818950248917947"]},"metadata":{"tags":[]},"execution_count":54}]},{"metadata":{"id":"RWcInvunj8K6","colab_type":"text"},"cell_type":"markdown","source":["### 布里尔分数可视化"]},{"metadata":{"id":"3WdYJO9Of_7g","colab_type":"code","colab":{}},"cell_type":"code","source":["import pandas as pd\n","name = [\"Bayes\",\"Logistic\",\"SVC\"]\n","color = [\"red\",\"black\",\"orange\"]"],"execution_count":0,"outputs":[]},{"metadata":{"id":"ASk0eNTzf_7i","colab_type":"code","colab":{}},"cell_type":"code","source":["df = pd.DataFrame(index=range(10),columns=name)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"yNj1XhtNf_7k","colab_type":"code","outputId":"79039524-f0a8-4ffa-90b5-5b65d1748c6a","executionInfo":{"status":"ok","timestamp":1547539899913,"user_tz":-480,"elapsed":920,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":359}},"cell_type":"code","source":["df"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Bayes</th>\n","      <th>Logistic</th>\n","      <th>SVC</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Bayes Logistic  SVC\n","0   NaN      NaN  NaN\n","1   NaN      NaN  NaN\n","2   NaN      NaN  NaN\n","3   NaN      NaN  NaN\n","4   NaN      NaN  NaN\n","5   NaN      NaN  NaN\n","6   NaN      NaN  NaN\n","7   NaN      NaN  NaN\n","8   NaN      NaN  NaN\n","9   NaN      NaN  NaN"]},"metadata":{"tags":[]},"execution_count":57}]},{"metadata":{"id":"_Z9MQcoff_7l","colab_type":"code","colab":{}},"cell_type":"code","source":["for i in range(10):\n","    df.loc[i,name[0]] = brier_score_loss(Ytest,prob[:,i],pos_label=i) #标签为i的时候的贝叶斯下的布里尔分数\n","    df.loc[i,name[1]] = brier_score_loss(Ytest,logi.predict_proba(Xtest)[:,i],pos_label=i) #标签为i的时候逻辑回归下的布里尔分数\n","    df.loc[i,name[2]] = brier_score_loss(Ytest,svc_prob[:,i],pos_label=i) #SVC下的布里尔分数"],"execution_count":0,"outputs":[]},{"metadata":{"id":"cfl44xg9f_7n","colab_type":"code","outputId":"8e268be4-1f67-4947-f108-7788964dd2a8","executionInfo":{"status":"ok","timestamp":1547539918219,"user_tz":-480,"elapsed":1100,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["df.shape[1]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3"]},"metadata":{"tags":[]},"execution_count":59}]},{"metadata":{"id":"2sk4kic5f_7q","colab_type":"code","outputId":"239be394-b73d-40f2-8988-7aae8f546793","executionInfo":{"status":"ok","timestamp":1547539928514,"user_tz":-480,"elapsed":1229,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":347}},"cell_type":"code","source":["for i in range(df.shape[1]):\n","    plt.plot(range(10),df.iloc[:,i],c=color[i])\n","plt.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAeEAAAFKCAYAAAAqkecjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfwEG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S7vVIvy/mrrne8UVVzB16lQuv/xyRowYwYgRI5rdv6rK194mW5Sb66a8vC6mxxRN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\n8KME887CWvUF6aumgrbHKi/Fr6KG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rnWWMpLc3FycPXsWAHDlyhXk5uZqnKPU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\n+H/TV62rQ8itDgAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7f411cf9c358>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"RxHrNPB6f_7t","colab_type":"code","outputId":"d049fc90-5943-4958-90c1-40886cf2c2fd","executionInfo":{"status":"ok","timestamp":1547539933869,"user_tz":-480,"elapsed":1158,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":359}},"cell_type":"code","source":["df"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Bayes</th>\n","      <th>Logistic</th>\n","      <th>SVC</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.00368051</td>\n","      <td>8.63311e-06</td>\n","      <td>0.0977746</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.0326197</td>\n","      <td>0.0114227</td>\n","      <td>0.23819</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.040738</td>\n","      <td>0.0026691</td>\n","      <td>0.150612</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.0242275</td>\n","      <td>0.00573814</td>\n","      <td>0.342311</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.0137171</td>\n","      <td>0.00169625</td>\n","      <td>0.166205</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.0127707</td>\n","      <td>0.00423538</td>\n","      <td>0.230307</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.00890695</td>\n","      <td>0.000908444</td>\n","      <td>0.17735</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.0280067</td>\n","      <td>0.00176068</td>\n","      <td>0.18596</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.0680707</td>\n","      <td>0.0100405</td>\n","      <td>0.497354</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.0315248</td>\n","      <td>0.00871922</td>\n","      <td>0.283608</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        Bayes     Logistic        SVC\n","0  0.00368051  8.63311e-06  0.0977746\n","1   0.0326197    0.0114227    0.23819\n","2    0.040738    0.0026691   0.150612\n","3   0.0242275   0.00573814   0.342311\n","4   0.0137171   0.00169625   0.166205\n","5   0.0127707   0.00423538   0.230307\n","6  0.00890695  0.000908444    0.17735\n","7   0.0280067   0.00176068    0.18596\n","8   0.0680707    0.0100405   0.497354\n","9   0.0315248   0.00871922   0.283608"]},"metadata":{"tags":[]},"execution_count":61}]},{"metadata":{"id":"89DzUolbkZy7","colab_type":"text"},"cell_type":"markdown","source":["## 2 对数似然函数Log Loss"]},{"metadata":{"id":"j1SqxvCgf_7v","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.metrics import log_loss"],"execution_count":0,"outputs":[]},{"metadata":{"id":"bK1Rb7pWf_7x","colab_type":"code","outputId":"cc5662a1-b6ac-4a7e-d735-5dce32f2f277","executionInfo":{"status":"ok","timestamp":1547539956998,"user_tz":-480,"elapsed":849,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["log_loss(Ytest,prob)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2.4725653911460683"]},"metadata":{"tags":[]},"execution_count":63}]},{"metadata":{"id":"jC8dOSh3f_7z","colab_type":"code","outputId":"0f355864-c633-4971-ff55-85b81843a9d5","executionInfo":{"status":"ok","timestamp":1547539960842,"user_tz":-480,"elapsed":1095,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["log_loss(Ytest,logi.predict_proba(Xtest))"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.12760008166154135"]},"metadata":{"tags":[]},"execution_count":64}]},{"metadata":{"id":"ShSKcOnrf_75","colab_type":"code","outputId":"5b738556-54d6-4e56-853d-e83f842b5f61","executionInfo":{"status":"ok","timestamp":1547539966345,"user_tz":-480,"elapsed":1207,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["log_loss(Ytest,svc_prob)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.625556312147472"]},"metadata":{"tags":[]},"execution_count":65}]},{"metadata":{"id":"HbUlyiXnkjiQ","colab_type":"text"},"cell_type":"markdown","source":["## 3 可靠性曲线Reliability Curve"]},{"metadata":{"id":"jWittHkjf_7_","colab_type":"code","colab":{}},"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn.datasets import make_classification as mc\n","from sklearn.naive_bayes import GaussianNB\n","from sklearn.svm import SVC\n","from sklearn.linear_model import LogisticRegression as LR\n","from sklearn.metrics import brier_score_loss\n","from sklearn.model_selection import train_test_split"],"execution_count":0,"outputs":[]},{"metadata":{"id":"brsdUVJOf_8G","colab_type":"code","colab":{}},"cell_type":"code","source":["X, y = mc(n_samples=100000,n_features=20 #总共20个特征\n","          ,n_classes=2 #标签为2分类\n","          ,n_informative=2 #其中两个代表较多信息\n","          ,n_redundant=10 #10个都是冗余特征\n","          ,random_state=42)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"SVW1hXM9f_8K","colab_type":"code","outputId":"2d69e8d7-0ef3-42f2-9feb-d629fa4e0f20","executionInfo":{"status":"ok","timestamp":1547540006278,"user_tz":-480,"elapsed":1076,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["X.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(100000, 20)"]},"metadata":{"tags":[]},"execution_count":68}]},{"metadata":{"id":"N08hgDcCf_8N","colab_type":"code","outputId":"985a9535-2238-4ce9-e776-116d1039b902","executionInfo":{"status":"ok","timestamp":1547540009144,"user_tz":-480,"elapsed":1216,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["np.unique(y)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1])"]},"metadata":{"tags":[]},"execution_count":69}]},{"metadata":{"id":"j164s3o_f_8P","colab_type":"code","colab":{}},"cell_type":"code","source":["#样本量足够大，因此使用1%的样本作为训练集\n","Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, y\n","                                                ,test_size=0.99 #训练集会很小，测试集会很大\n","                                                ,random_state=42)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"gxkQ_TAqf_8Q","colab_type":"code","outputId":"6a6fcb05-9cc9-4e18-bfd6-be22777428fc","executionInfo":{"status":"ok","timestamp":1547540017374,"user_tz":-480,"elapsed":922,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["Xtrain.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1000, 20)"]},"metadata":{"tags":[]},"execution_count":71}]},{"metadata":{"id":"Dxh3FB4Xf_8R","colab_type":"code","outputId":"e9690f37-f364-4547-94c4-cbbfe2266267","executionInfo":{"status":"ok","timestamp":1547540018967,"user_tz":-480,"elapsed":556,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["Xtest.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(99000, 20)"]},"metadata":{"tags":[]},"execution_count":72}]},{"metadata":{"id":"r6bs7qRUf_8U","colab_type":"code","outputId":"62efcf0a-6c80-4f35-a77c-c9d2aa21f935","executionInfo":{"status":"ok","timestamp":1547540021110,"user_tz":-480,"elapsed":848,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["np.unique(Ytrain)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1])"]},"metadata":{"tags":[]},"execution_count":73}]},{"metadata":{"id":"IYPxXkwxf_8X","colab_type":"code","colab":{}},"cell_type":"code","source":["gnb = GaussianNB()\n","gnb.fit(Xtrain,Ytrain)\n","y_pred = gnb.predict(Xtest)\n","prob_pos = gnb.predict_proba(Xtest)[:,1] #我们的预测概率 - 横坐标\n","clf_score = gnb.score(Xtest,Ytest)\n","#Ytest - 我们的真实标签 - 横坐标"],"execution_count":0,"outputs":[]},{"metadata":{"id":"3zmqiz14f_8Z","colab_type":"code","colab":{}},"cell_type":"code","source":["#在我们的横纵坐标上，概率是由顺序的（由小到大），为了让图形规整一些，我们要先对预测概率和真实标签按照预测概率进行一个排序，这一点我们通过DataFrame来实现\n","\n","df = pd.DataFrame({\"ytrue\":Ytest[:500],\"probability\":prob_pos[:500]})\n","#利用字典来创建DataFrame({\"列的名称\":[列的值]})"],"execution_count":0,"outputs":[]},{"metadata":{"id":"iHjhCfcDf_8e","colab_type":"code","outputId":"8912140d-5c49-4ccd-929d-e7fd86b42347","executionInfo":{"status":"ok","timestamp":1547540050601,"user_tz":-480,"elapsed":1112,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":1969}},"cell_type":"code","source":["df"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>probability</th>\n","      <th>ytrue</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>9.999997e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9.999964e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>9.886612e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>8.441773e-18</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>9.858597e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>4.473008e-07</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>9.999023e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1.473024e-03</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1.874007e-09</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>9.999987e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>9.689937e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>3.103837e-04</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>4.271818e-02</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>9.999959e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>3.186196e-06</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>9.994839e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>3.983813e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>3.909404e-06</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>2.915660e-10</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>2.791774e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>9.999742e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>1.033499e-05</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>470</th>\n","      <td>9.999988e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>471</th>\n","      <td>2.424698e-02</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>472</th>\n","      <td>4.938448e-12</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>473</th>\n","      <td>1.112100e-11</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>474</th>\n","      <td>1.313424e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>475</th>\n","      <td>5.498802e-05</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>476</th>\n","      <td>9.983382e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>477</th>\n","      <td>3.921403e-06</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>478</th>\n","      <td>5.504136e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>479</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>480</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>481</th>\n","      <td>9.998888e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>482</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>483</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>484</th>\n","      <td>4.309378e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>485</th>\n","      <td>9.999766e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>486</th>\n","      <td>9.998858e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>487</th>\n","      <td>3.373063e-06</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>488</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>489</th>\n","      <td>7.445310e-02</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>490</th>\n","      <td>9.999987e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>491</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>492</th>\n","      <td>7.151488e-03</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>493</th>\n","      <td>1.579079e-05</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>494</th>\n","      <td>9.999999e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>495</th>\n","      <td>9.224911e-04</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>496</th>\n","      <td>9.931903e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>497</th>\n","      <td>5.670253e-06</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>498</th>\n","      <td>8.248345e-08</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>499</th>\n","      <td>4.655974e-16</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>500 rows × 2 columns</p>\n","</div>"],"text/plain":["      probability  ytrue\n","0    9.999997e-01      1\n","1    9.999964e-01      0\n","2    1.000000e+00      1\n","3    9.886612e-01      1\n","4    8.441773e-18      0\n","5    9.858597e-01      0\n","6    4.473008e-07      0\n","7    9.999023e-01      1\n","8    1.473024e-03      0\n","9    1.874007e-09      0\n","10   1.000000e+00      1\n","11   9.999987e-01      1\n","12   9.689937e-01      0\n","13   1.000000e+00      1\n","14   3.103837e-04      0\n","15   4.271818e-02      1\n","16   1.000000e+00      1\n","17   9.999959e-01      1\n","18   1.000000e+00      1\n","19   3.186196e-06      0\n","20   9.994839e-01      0\n","21   3.983813e-01      1\n","22   3.909404e-06      0\n","23   2.915660e-10      0\n","24   2.791774e-01      0\n","25   1.000000e+00      1\n","26   9.999742e-01      1\n","27   1.000000e+00      1\n","28   1.033499e-05      1\n","29   1.000000e+00      1\n","..            ...    ...\n","470  9.999988e-01      1\n","471  2.424698e-02      0\n","472  4.938448e-12      0\n","473  1.112100e-11      0\n","474  1.313424e-01      1\n","475  5.498802e-05      0\n","476  9.983382e-01      1\n","477  3.921403e-06      0\n","478  5.504136e-01      0\n","479  1.000000e+00      1\n","480  1.000000e+00      1\n","481  9.998888e-01      0\n","482  1.000000e+00      1\n","483  1.000000e+00      1\n","484  4.309378e-01      0\n","485  9.999766e-01      1\n","486  9.998858e-01      1\n","487  3.373063e-06      0\n","488  1.000000e+00      1\n","489  7.445310e-02      1\n","490  9.999987e-01      1\n","491  1.000000e+00      1\n","492  7.151488e-03      1\n","493  1.579079e-05      0\n","494  9.999999e-01      1\n","495  9.224911e-04      0\n","496  9.931903e-01      1\n","497  5.670253e-06      0\n","498  8.248345e-08      0\n","499  4.655974e-16      0\n","\n","[500 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":76}]},{"metadata":{"id":"iZIv5-6bf_8h","colab_type":"code","colab":{}},"cell_type":"code","source":["df = df.sort_values(by=\"probability\")"],"execution_count":0,"outputs":[]},{"metadata":{"id":"y-3Z6y1rf_8i","colab_type":"code","colab":{}},"cell_type":"code","source":["df.index = range(df.shape[0])"],"execution_count":0,"outputs":[]},{"metadata":{"id":"v317kYxLf_8j","colab_type":"code","outputId":"93b35e43-46f3-4db9-c952-ba9955259506","executionInfo":{"status":"ok","timestamp":1547540088522,"user_tz":-480,"elapsed":806,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":1969}},"cell_type":"code","source":["df"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>probability</th>\n","      <th>ytrue</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>9.999997e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9.999964e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>9.886612e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>8.441773e-18</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>9.858597e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>4.473008e-07</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>9.999023e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1.473024e-03</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>1.874007e-09</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>9.999987e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>9.689937e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>3.103837e-04</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>4.271818e-02</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>9.999959e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>3.186196e-06</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>9.994839e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>3.983813e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>3.909404e-06</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>2.915660e-10</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>2.791774e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>9.999742e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>1.033499e-05</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>470</th>\n","      <td>9.999988e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>471</th>\n","      <td>2.424698e-02</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>472</th>\n","      <td>4.938448e-12</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>473</th>\n","      <td>1.112100e-11</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>474</th>\n","      <td>1.313424e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>475</th>\n","      <td>5.498802e-05</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>476</th>\n","      <td>9.983382e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>477</th>\n","      <td>3.921403e-06</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>478</th>\n","      <td>5.504136e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>479</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>480</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>481</th>\n","      <td>9.998888e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>482</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>483</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>484</th>\n","      <td>4.309378e-01</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>485</th>\n","      <td>9.999766e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>486</th>\n","      <td>9.998858e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>487</th>\n","      <td>3.373063e-06</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>488</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>489</th>\n","      <td>7.445310e-02</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>490</th>\n","      <td>9.999987e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>491</th>\n","      <td>1.000000e+00</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>492</th>\n","      <td>7.151488e-03</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>493</th>\n","      <td>1.579079e-05</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>494</th>\n","      <td>9.999999e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>495</th>\n","      <td>9.224911e-04</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>496</th>\n","      <td>9.931903e-01</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>497</th>\n","      <td>5.670253e-06</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>498</th>\n","      <td>8.248345e-08</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>499</th>\n","      <td>4.655974e-16</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>500 rows × 2 columns</p>\n","</div>"],"text/plain":["      probability  ytrue\n","0    9.999997e-01      1\n","1    9.999964e-01      0\n","2    1.000000e+00      1\n","3    9.886612e-01      1\n","4    8.441773e-18      0\n","5    9.858597e-01      0\n","6    4.473008e-07      0\n","7    9.999023e-01      1\n","8    1.473024e-03      0\n","9    1.874007e-09      0\n","10   1.000000e+00      1\n","11   9.999987e-01      1\n","12   9.689937e-01      0\n","13   1.000000e+00      1\n","14   3.103837e-04      0\n","15   4.271818e-02      1\n","16   1.000000e+00      1\n","17   9.999959e-01      1\n","18   1.000000e+00      1\n","19   3.186196e-06      0\n","20   9.994839e-01      0\n","21   3.983813e-01      1\n","22   3.909404e-06      0\n","23   2.915660e-10      0\n","24   2.791774e-01      0\n","25   1.000000e+00      1\n","26   9.999742e-01      1\n","27   1.000000e+00      1\n","28   1.033499e-05      1\n","29   1.000000e+00      1\n","..            ...    ...\n","470  9.999988e-01      1\n","471  2.424698e-02      0\n","472  4.938448e-12      0\n","473  1.112100e-11      0\n","474  1.313424e-01      1\n","475  5.498802e-05      0\n","476  9.983382e-01      1\n","477  3.921403e-06      0\n","478  5.504136e-01      0\n","479  1.000000e+00      1\n","480  1.000000e+00      1\n","481  9.998888e-01      0\n","482  1.000000e+00      1\n","483  1.000000e+00      1\n","484  4.309378e-01      0\n","485  9.999766e-01      1\n","486  9.998858e-01      1\n","487  3.373063e-06      0\n","488  1.000000e+00      1\n","489  7.445310e-02      1\n","490  9.999987e-01      1\n","491  1.000000e+00      1\n","492  7.151488e-03      1\n","493  1.579079e-05      0\n","494  9.999999e-01      1\n","495  9.224911e-04      0\n","496  9.931903e-01      1\n","497  5.670253e-06      0\n","498  8.248345e-08      0\n","499  4.655974e-16      0\n","\n","[500 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":78}]},{"metadata":{"id":"fq7zDVIif_8o","colab_type":"code","outputId":"077ae174-f7c7-4e3e-b74a-719f846f2297","executionInfo":{"status":"ok","timestamp":1547540094448,"user_tz":-480,"elapsed":1586,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":361}},"cell_type":"code","source":["#紧接着我们就可以画图了\n","fig = plt.figure() #画布\n","ax1 = plt.subplot() #建立一个子图\n","ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\") #得做一条对角线来对比呀\n","ax1.plot(df[\"probability\"],df[\"ytrue\"],\"s-\",label=\"%s (%1.3f)\" % (\"Bayes\", clf_score))\n","ax1.set_ylabel(\"True label\")\n","ax1.set_xlabel(\"predcited probability\")\n","ax1.set_ylim([-0.05, 1.05])\n","ax1.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe8AAAFYCAYAAAB6RnQAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ24UwdLUEMPDXfT+fj7bk0j+vfWt7uW51XOpm9+M0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KKvjw0ZstQHxlwDrJ0bDmtzDo5c\nLcSNNM0inVa+GD8pJuNF+dt1S2N71feq8YjfrhrS8xPupRRVtmDjviQ0t4owf5Qvpg/z1lsrZWJO\nDb7anwweX4yFY/0xf7Qf5do5dq2QEr179/kIslUKkHuP6mMs318UiYkDNdX+OhLpL9egT9Kh6lwi\nleHglXz8eCSNDIe/MlkeJt99NpusMl82ORhLJwahurEdn+yKR2JuLTklryucbU2wfkk0xvV312lD\nxBdKcOZu6QOf15HxA9y1CmGpE+RhBX83S1xMKKfcz+rkvJ6kUadXagWdFqypGW+uIYsUWAFU40B5\niovT1NgQTCYDc0f6wsXWBLvPZuP7v1LJvMzG1wbhvV9uk69Xzj+2tzSCoQETsckVyCySS6B2JaDw\nIFlUJpOBqUO8EOhuhV9PZeD49SJklzRi2ZQQrbvanNImyAgCwXSVOcmh2HwQkA8SKanm4XZGNdwd\nTDFIixjO6VvFaGmT5/AGhThStKZb+WL8dTkfhgZMLBznr/f+eSszDtbODccXexOx60w2zE0MEeaj\nMtCrf7hO/r1qZh+dj7tBzaP58Y1hz2zff1ZJo9xoiaVYMiFQL3oKgDwacy6uDIeu5MPQgIX/zApD\nuK9qqItMRmD9zjiUq6XHvl01hPL77ahh/sPqoTDTMQRub2UMN3tTZBY3oF0g6XTT3tAiwC8nMpBf\n0QwHKyOsnNEHbvamuF/Xhl9OpKO8tg1uSjEWKyNcTanAvgt5pLIgv5OIppLRka6YM9JH59G3N+9V\n4veHCGe/PrMPTt8qJodAdcZ/ZvXBB7/dpdxnbc4hO4Q6YmnK6TJv/iihPW8FnV1kApG68WZTPG/l\nBd/aLl+41ceBDunjhHcWRMLUSPWjsDLl4KUJqp5wJTVNfHzwQj9MGOCO2iY+vtiXiMOxKmnNjqgb\nb/WFuiP+bpZYv6Q/IvxskV3ahI93xCElXzNMppJEpUPmgPzzSC+U93GHeFnjL0XOd94ozVBpZX0b\n6QWwmAzMGUktUjscmw9euxjThnrpVbxDHWdbE6yeHQYWi4Etx++hUFFc9tMR1cSkn9YM0/l4Z+6W\nkLnJd5+P0Llu4t9GSl4dvj+YColEhhXTQvVmuKUyGfaez8XBK/kwNzXEewsjKYZbIJLgla+uUAz3\nr+tGUAz3lmP3SMPNMWRh+7sjdTbcSqIC7CCREkgr1B46Tyuox/qd8civaEb/IHt89FI03OxNcfNe\nJT7ZHY/y2jaMjHTBfxdHwcqUg9//lo8ylsq0r1vqmHDZeGN2GBaO89fJcIslUizdePmhDDcAbD56\n74GGe/s7I3E4tkBDQyHCV3u0zNzEkFz7nwS08Vagizwqx5CF+haV8TZR9Njy2uWed8dxoL6uFvjw\nxWjy9vcHU8gL6A01MQVA3kPbP8gB7y6MhI05F//cKcGnuxNQXkMNkcsIgpwGBDx4DKipkQFen9kH\nC8f6QyCS4sfDadh/MZcyqzy0LdJFAAAgAElEQVSzpBGGBkx4a5lF/ayhLoM6Z4QvknLrkFvejHBf\nW41hLQRB4MBF1bjPOSN8YGmqWmBzy5pwLbUSrnYmGNtPM3WiT/xcLfHa1BCIJTJsOpSKC/FlSFbk\nM1fPCiOv1QdRVNlCvv8pgz01Buw8K9zJqMLmo/fAZMh/q/0C7fVyXHl/+D2FIpsJPlzcj9J5UN8s\noITBHa2NseO9UeS0QqlMhqUbLyMhRx7ujunrjK1rhz9U/j2KrDqnhs6lMhmOXC3ApkOpEIgkeGGc\nP5ZPDQGDAfx2KhO//50FFpOBldND8cK4ANQ1C/DZngTcSpfLAT+o0aWPtw0+WzYQfX11Gx+bnFtL\nRim7Q0xfJ53bZn98YxhyyppwOamCcr+rnQnZQdIRfzdLSuvw44YOmysQSWSQSGUaIz07hs3V0Qyb\nay6Qlmaq3XB2aRMpkOFia4KpQzxx8mYx+fiGXfHoF2CH9UuicfBKAa6l3scnu+MxI8Ybz0W7kxPI\n1KMBXXneShgMBkZHucLP1QLbTmbgYkI5csua8Nq0UHAMWLhf14ZQb2u9tbz0ZuIyFTKoIQ5wsTPB\n1hPpWj1qQO6ZKQvBnG1NMEqtZ1YileGPc3KJ1MXPBXY6KlafRPjb4YXnArDnbA4OKKIFYT42CPfT\nbZFsF0jw6W55P7etBRczYnpWFd9buZJUjr3nc8HlsLFmTpjGyM2HpZEnxA+HUlFa04pQL2usmB5K\nKV7MLWvCRrWBL5MGeWCWWrthI09IGd+6YnooonuwqXC2NSHlS5VOSiNPiG0nM5Bb1gQ7Sy5WTu8D\nD0czlFbz8MuJDFQ1tMPLyQzLp8lHe97NrMaus9mU4q6uWDjWH6MiXXRKw0hlMqz+4cYDQ+8dMWAz\n8cK4AEQF2FHqNgC5A9bxXNcviQaLycDOfzS9+qlDvEhthI642pogQesjjwfaeKvRLpBo9LCq93l3\nVFBShs15yrC5lrCVsiezf5A9HKyMcepWMQCgvLaNHAX6ydL+2HQ4FQ0tQiTk1CIhpxbrl0Qjws8W\nO89k49CVAqTk1eHlycEo61Cs1h2j4O5gho9ejMb+i7m4nlaJDTvj4esiFxGh890KGdRrchnUmcO8\ncSWpAjWNfIyOdIWTjQnluSKxFPvVvO6FY/wo38X5+DJU1LVheLjzIxkA0Rkjwl2w56xKV325jv3c\nBEHg9U2qhe7L1wbp/dx6A3/fLsaRq4UwM5aP9NQmxPMwlFbz8MPhNDTyhBge7oyFY/2p10tcKf5U\nK3pcM6cvZWOeUdRAGYC0cflA2Fv1bHgQgyFXOfvnTgkyihpQ1yrCV38kgNcuRpS/HZZMDIIRh4Ur\nyRU4cFGexx4X7YbZI3xAEMAf53NwJUm7V8pmMSCRqlxwVzsTLJ8aQs6sfxDZJY346kByt9+TvZUR\nVs3oA2MOW8NwmxoZoJUvhru9KRm9fHVqMNwdzLDnXI5GNby8a6fzgj4L0yerd0AbbzVa+WJN462e\n8+6Qm1HumsmwuZbcoDJ/Ym0u92SUxvuno2kgCHme1MnWGN+sHIKE7Bpyl7d+ZzwGBjvgk6X9sfd8\nDhJyavHxjjhweugdcwxZWDIxCMGe1th9NhsZiv5udSWwZxV1GVQuh42TN+XylFOHemo892xcKZlC\niQ60p/Q/1zbxcfJGEcyNDTB7hKbH/ij55QTVS9h89B7enNv3gZs89RnPP6159grUCILA4asFOHOn\nFNbmHLw9P0JD3ORhSSuox9YT6RCKpJgzwgfjB7iTny9BEPj6QDIZkQPkGyf1qugjVwvw921VK+C2\nt4frrKr3IKIC5MZ789F7YDDknSsLxvhhTJQr+EIptp7IQEJ2DUy4bKycEYpwX1vUNfGx5Xh6pzlk\nFpNquMdFu2HWcG+dzpkgCHz4e9xDDd+JCrDDkglBqG3iY91WTcGWVr4Y/YPsEZelEqcZGOyIjKIG\nxKqFxpVS17NH+uBztXn36hhz2I8lmtYVtPFWQ1tLg7CLsLnytvJ12jxvco63GQcSqbwf3MCACSMO\nG82tIkhlBGQygMUE+gXa4+c3Y/D+r3fQ0ibCncxq3Mmsxicv90eEvx32ns9FS7vqHAPdHz6cNyDY\nAV7O5mT1+46/s/Da9BB4Oj6bRrytgwzq6VvFaBNIMHekr8b3WtfMx/HrReTteaNU/c9KsRaRRIYX\nJwTqnGvWBxlFDeTCtGJ6KG6nVyElvw47/s7CK1OCO82Lnr5VTIr+/N+iqMd6zk8DMhmBvedzEJty\nHw7Wxnh7Xjg5rrenxCZXYO/5XDCZDI0wt0gsxWvfUnO5v7w1nCzgIggC7/96hxyw4e9qgffU+rv1\ngbVaEZyVGRcrp4fC29kcRZUt+OVEOmqbBPBztcDyqSGwNuciJb8Ov5/O1JrrVXrbSg0KjgELq2aG\nItRLt/bE4qoWfLLr4QLR80f7YWw/V6QV1OOHw6pCTXMTQ7ITZFy0G87Hl5GPMRhy1bqdZ1ThclsL\nLuqaBYgKsENLFypwvq4WZOT0SUEnOdVo02K8KTlvjsp4mxoZkLtnZdhcW1Vug8I7szbjoKFFAAJA\nvwB7vLMggnzO138mkxeYEYeNTf8ZSgl3fvR7HDKKGrBhaTTl2D1dZKVq1ew1TXx8vicR5+PLui2t\n+m/gn9sKGdTBHmgTiHEpsRy2Flyt2s8H1cKbs4Z7U4Y7JObUIq2gHsGeVhioGATxOOALJWRYNcjD\nCtGB9lg+LQS+Lha4k1mNQ1fytb4uv6IZR68VApCLUzzOEP/TgEQqw2+nMxGbch9u9qZ4b2GkXgy3\njCBw8HI+9pzLgTGXjXeej6AY7qZWIcVwmxoZYMd7o0jDzRfKhVeUhnveKF+9G+7skkaKyMnLU0Pg\n5WSG83Gl+N8fiahrEmDyYA+883wELEwNcSg2Hz8eTtNquE24bIq3HeFni69WDNLJcBMEgW/+TH5o\nwx3T1xnjot1wJbmCYrhnxniT6yoAiuGO8LMFQQC//51FtoGZmxhCIpUpppj5kPoI2vB3s0T1E55a\nRhtvNVq1CLWoTxVTVz0yVis0aW0Xg2vI0lrw1aAWNlfmVGwtuJRq7/zyZny6Wz4HV8mAYAf8/GYM\nqeJ2K70K72y9DXUSc2vx26nMTseZPgilJOpLEwKxdm5fmHDZ+PNSHn44nIaWJ9gC8bipbxbgQoJc\nBnVMlCsOXymAVEZg9ggfje80s7iBrPS1tzLCuGiV2hpfKMH+i7lgs+QFM48z9Kye31un2BhyDFhY\nPTsMTjbGOBdXhrMdhC3aBWL87w95WNDJxhhThng9tvN9GhCJpdh89B7uZlbD19UC7z6vn5GeIrEU\nW4+nk7Oq/7s4iqKmWFzVgrVqE79GRbrgxzdUrXyl1TzK9/nBC1FaVf0eFhlB4NStYnz9ZzJa+WKy\nTe1m2n38dOQe/rycDxMuG2vnhWNmjA947WJ8fSAFZ+5oF0ZhgDqwafH4ALw+s49OrWuV9W14+csr\n5Fr0MFiaGuLPS3mUoU+LxweQo1A7su3tEeT3nKymMOdqZ4KmVhFiwp0p67s2/N0sn1h/txLaeKvR\nxtfcUQo79HkrMVITNeDxxRptYkrUw+bKHnEbCy4Zcpk70hczhnmhvkWI//2RSFE8MuKw8dOaGCyb\nEqz12F5O5ridUYUPf49DhqJXuzuQ/d0eVgj1tsGGpf0R7GmFtIJ6fLwjjpzv/W9HXQa1qJKHxNxa\n+LiYa1TySqQyin75wrH+FON+7FohmlpFmDTIAw56ypfqwnY1jfxN/6GO6jQ1MsCbc/vC0tQQB6/k\n406Gsp2HwOubVAIun70y4PGc7FMCXyjBdwdTkVZQjxAva7w1N7zb41W10dImwlcHkpGYUwt/N0t8\n8EIUpbDseup9ioe5akYoFo0LIG9fTamgeMM/vjEMPi76i4a0tIuw6WAqjl0rhKUpB+8ujMTrCvGe\nW2mVSMmvQ5CHFTYs7Y8QL2tkKbxzbTLLyoItpb/tYmuCz5cNwIhw3arJt5/O1BBE0ZUpgz3x+TL5\nNXvyZjHFq145PRRXkipwv65NI3L27aohMGAzweqQr/Z1tUBFbRsMDZiYOsST4sF3xJDNhKejGSrr\nn6zxpnPeamjLeXfWKqbcmREEAV67qNOq1AaeACwmA+bGhmqetxGKq+RCGvZWRoj0t4OzrQl+O52J\nzUfvYWaMNyYN8iB/AINCHNHXx5ZSDQwASycFITGnBqduFuPbP1MwOsoVs0f4UCIEnSGVyZBd2gR7\nSyPYKopjLEw5WDsvHGfvluLYtUJ8cyAZkwZ7YtpQT7A6DjL/l1BazcOt9Cq42ZtiYIgD6YnOH6U5\n6ONyUgX5g43ws6VIjRZXteBSUjkcrI11kqfUF9kljWR/7WvTQrRO/LK1MJKrsO1Lwu9/Z8HMxBB7\nz6kq0jeviXmmCtR47SJ8dzAVJVU8RAXY4dUp+pkMVlnfhu8PpqKuWYCBIQ5YMiGIPC5BEGTxl5LP\nlw2gdDH8dCSN9ARNuGy9K9vlljVh28kMNPKE6ONtg1cmB8HEyABn1HTxQ72ssWZOX4ABnLpVjOPX\nC7X2bVuYGFKmm00Y6I4Zw7x1KuKqbxZoLSjTBRMuG8umhCDMx0ariNXb88Nx5GohympaMSLChTID\n4r2FkaTQzaVElfwpm8WAg6UR8subMXmwJyxMDFFU2dLpOXg7m6NdKNEYWvW4oY23GtoL1lQXiPqF\noGwTE4ikkEiJTlWoGnlCWJpywGQySONtY8FFfJY8pGOvMJxRAfawszTCj0fScPRaIe7XteGlCYFk\nDsyYy0a/QHvKj//D7XcxIsIF//dCFLafzsSlxHJkFDVg2ZRgyvB6bRRX8cAXSjAgiOpdMhkMTBzo\ngQA3S2w7mYHTt4qRXdqI5VNC9FbE8zRx+GoBCMgFVuKzalBUyUP/IHsNb6e5TUQqrQHyAhklMhmB\n3WdzQBDA4ucCHlu/vFAkJdtpfF0s0D+o8xy7q70pVs/qg2//SsG3aiMjP1gc9Uzp2TfyhPjmz2RU\n1rdjaB8nvDghQC8b0+ySRmw+eg/tQgmmDvHEtKFepOGVSGXkQCIlW9bGkJG8jo+PjHTBC2reeE+R\nEQTO3i3F0avy2oZZw70xYaAHeO1icsa2EicbE7QLJfjtVCbuFdZrHMuYw0a7UIJmtVzyugURGgJG\nnXHwSr5G+kZXvJzMsXJ6KGwsuJDJCI3P9MMX++HPS3koqmzB4FBH+DibUxTZlPUcqR1UJoeFOeN2\nRhVMjQwwYYA7zsWVoSv83SxR0/Bk890AHTan0LFgjSAIiuet/rgyF83ja1dXA+SLehNPBCtz+W6v\nvpkPBkNevFajCJurt4S4O5jhwxej4eNijjuZ1fhyfzKa1KaYadM0j02uwKe7E7BieijG9nNDVUM7\nPt+TSIaCO0OZY+psxKOPiwXWL4lGv0B75Jc34+MdcRpKTL2dDDUZVH83Sxy+WgA2i0ERxlByWKF1\nDgDThnpRvrdLSeUoqeJhUIijzouYPljxnarg6f1FkQ98foC7FUaoTTsbEe4Mn2dIVa+msR1f7E0k\np3e9NDFQL4b7Vnolvv0rBUKxFC9PCqIMLuG1izSMzO/vjiQNd0OLgPL4qhl99Gq4W/li/Hg4DYdj\nC2BmYoB1C8IxaZAnckoasX6HvBA2zMcG364aAhMuGxcSyrB+Z5xWw+1obUyZvhjpb4cf3xim0zXf\n0i7C0o2XH9pwj450JYsJhSIpXvnqCuXxDUv743BsAfLKmxEdaI/RUa4aUqqt7WK0CcTYdTabcj9B\nEBCIpJgyxBNGHDYOdlLcqURerPZkQ+YAbbwpdJwsJpLIQFAeV124ylnepECLkRaBljYRZARBtmPU\ntQhgZcYBm8VETSMfFiaGGlNpLEwM8c6CSAwOdURRZQs+3Z2A4qoW8IUSsvIUAH5+M4aik/7R73GQ\nEQTWzQ+HlZkhTt4sxud/JHbaL5lV3AAGum43M+YaYMW0ELw4PgASqQw/H7uHP87nUDY0vRW5DKr8\nRzpnhC8uJJShoUWIsf3cNCYPFVQ04+Y9eWjaxpyDCQNUxUONPCGOXSuECZdNaRl71Ow6o1qAvnt9\niE7h1Va+mDItKbOk8ZkpTCyvacUXe5NQ1yzA9GFemDfKt8cjPQmCwPHrhdh+OguGBiysnReOIX2c\nyMcr6trwxo83yNsDgx2w471R5HeVVlCPt7eowscbXxuEqAD9TZ0rqGjG+p1x8ry+pxU2LOkPP1dL\nHL9eiG/+TEErX4y5I32xenYYLE0NyfVN2xAOEy4bVWoFWksnBmHVjFCddO/P3CnBGrXPoTtwDFhY\nPjUEC8fJ60uaWoWUTauSX06kI6ukERF+tnh+jB+pFGjEYWFMP3neu5EnxP4LeZRwPwBcT6uErQUX\nI8JdupzxDch72H2cLSifxZPi2YmXPQBto+s65jTUHzcih5J07nk38ORhcmWPdyNPCD8XC0ikMtS3\nCDotRDFgM/HypCC42pni0JV8bNybpKFhbsRhI6avM6IC7LB603UQkOdxLiWWY/2SaFyIL8PN9Cps\n2BWP2cN9MLqfK7lYCUVS5Fc0w93B7IEVoQwGA8PDXeDraolfTqTjSlIF8hTSqs62Jl2+9mkmLrMa\npdWtGBjiACszDv6+XQJTIwNMGuRJeZ5M0bet5Pmx1EEK+y/mQiCS4qUJgVrzzY+CvPImXEu9DwB4\nZXIQRU+9MwiCoEwYmzjQA//cKcEPh1KxbkGEhnrgv4mC+83YdDAVbQIJFozx04vOvEQqw85/snE7\nowq2FlysmdOX8nuIz67BVjVZzZcnBVEM+8HL+Tgbp/JCt709Qm/pFoIgcCG+DIdiCyCTEZg+zAuT\nB3miuU2Erw8kI6esCbYWXCyfFgIfZwvwhRLsPJOt9VgutiaoqGsjDbu9pRHenNcXDjqou/GFEg2V\ns+7gZGOMVTP6kJ9rRW0rPvw9jvJ4oIcVrihqUUK8rPHqlBCKcd+8JoYMg19OKsdtRcGmOlIZgZkx\n3jBgM/HFXu2iLEo8HM3AMWQ98TYxgDbeJCZcLca7gwauumdu3EFdTZuueaNiB2ttxkUjTwiCAGws\njFDfLABBqPLd2mAwGBg/wB2ONsb49WQGLqoVWFDP2wC/vzcKsckV2KMoQlq/Mx7jot2wakYf7D6b\njQOX8pCSX4elE4NgY8FFXnkTJFKiW1PEXGzlQxT+vJyP2OQKfLIrHs+P9cfM0f46H+NpQSyR4chV\nlQzq8RtFEIikWDjWRyP/eyOtklSS6uNtQ5n+lJpfh8ScWvi6WmBomBMeByKxFF/sletfeziYYXCo\nbv/3HbUCoZ/fjAHXkIXmViFupldh6/EM/GdWnyeuGPUoyCpuwI9H7kEkkWoY0IelTSDGz0fvIbu0\nCV5O5lg9O4zSYrbnXA5FsWv9kmiyoJUgCLyz9RY5WjjIw4ps7dMHbQIxdvydheS8OpibGGL5lGAE\neVojraAe209nopWvlD4NhDHXAGU1rdhy7J5WY+RqZ0KZbDZ5sCemDvHU6Tq5nnq/0w2BLgwMdsDi\n8QHkpjKzuAHfqNVqBLpb4j+zwiibg9dn9sEKtd75LWvlhZjKIrXravPuDdhMsl3X3cEU/YMdIJbI\nwBd2HVX0V+jc19Ce99ODqZEBymvbQBAEGdbq6HmrTx4jdc35nYfN1dvE1IvVlPnuroy3knBfW3zw\nQhRlx2mrpXBsRIQL+gXak97V+fgynI8vw38X98PpW8VIya/DRzvu4vkx/qSaVnAn+e7OMDRgYfFz\nAQj2sMKuM9nYdSYbBZU8zBuhafSeZi4nlaO+RYDn+rtBKJbiakoFHK2NMTycGt1oE4ix/6Ka1z3G\nj3Jt7LuQCxaTgcXPBfQ4BKsr6sIeH73UT6fXHLlaQBqLD1/sR8r6vjghEC3tYtwrrMfuM9lYOino\nX1V1npxbi60nMgAQWDk9FFEBPZ8MVtPEx6aDqahqaEeUvx1emRJMdndIZTKs+fEGJb22ec0wsgWt\noyeqryiAkqLKFmw9no66ZgEC3S2xfGoITIwMyCIxNouBReP8MTJC3sp1Pe0+/jiX22ltjLrhfm9h\nJPzdHqzoqE01TlcYAFgsBhaM9sOICFW72fW0+9j5j2ojEB1oj1cmB2PfBVXHxJgoV3yvpv3+xasD\nScOvPkpVydQhnjiiKOCbM0KeQtlz4cGbDX83SxAEQW521DcBj5ves+I+YkyNDCCRyiCSyMgfY1dh\nc5PuhM3NOWTu2daCS+au7ax0m+/sYmcKKzMOuRmoaxagoUVAUfZSvocd743CpcRyMtT72Z4ETBjo\njgj/QBy4mEcp4vB7SDWtfoH28HQyw68nM3E9pQJZRfVkCO5pp00gxmmFDOqkQZ747VQmCAKYO8pX\nw6M4fr0IIkW3Qcfe7ZM3i1DXLMCEge5w1XHYQk/Zrxa+/3aVbnnu7JJGUhd77khfShcCm8XEiukh\n+PpAMm6mV8HSjKO1WK83ciu9Ejv+zgabzcB/ZvVFSDc3qtooqGjGj0fSwGsX47n+bpgzUpU3bxdI\nNFo5t787kny8pIqHDbtU/dsfvtjvgR0hukIQBC4nVeCvy3mQSglMGSyvdm9oEeDLfUkouN8CBysj\nvDYtFB6OZvKN5/lc3LhX+cBjh/va4pXJwTptzhNzavDzMe0TuLrChMtGm0ACa3MuVs4IpXwuR68V\n4rRiHgQgd1IWjfXHgYt5uJZaSQ4bUY9MvjE7jPJb7dj25WRjDDd7VWtviJf82riW+uDPw9fVAs1t\nItI2WJtzn5hYy78vTvaQKAsv1CvKRWphczaLgWa1ym9VwVrnxpscSmLGJQVabNUEWnTxvAF5fk1d\n5g8APtmdgIKKZq3PHx3lSlFsOnOnFDv/ycaaOX3hopaXexhhFyW2FkZ4d2EE5o7xR32zABv3JuHM\nnRLInnJpVXUZ1OKqFtwrrEeQhxX6dhitWl7TSvaCmhsbYLJaLry8thXn48pga8HF1MekSlZ4v4Vc\noF6aEKjVm+gIr11EtpJ5OZlh/ABNlS6uIRtvzOkLeysj/H27hNL/2lu5lFiO7aezwDVk4e35EXox\n3AnZNfjqgFyR7IVx/pg3yo80zDWN7RTDHeptjR3vjSIfv5xUTjHcP60ZpjfD3S6QYOuJDOy7kAuu\nIRtvzuuLGTHeSM6rw/qd8Si434KBwQ746KVoeDiaKbpRErQabhc7ag3LsinBWD077IGGWyqT4dWv\nrzyU4bYwkRfKhfnY4OMl0ZTPZcuxexTDPWmQB14Y54/DVwtwKakcLnYm+LSDuNC0oV6UOeEt7SKc\nUhu7DMjFlY5dLyRvi8RSpBVQ28e04WpnAlMjA4qxttbhd/iooI23AhOF8Vb3rjuOA1UfCkIWrCln\neWsJmzfwhGAyGLAwMaQY7+563vfr2ig95s+P8QOvXYQv9yfjVrr23aLSC1+g1o+8cV8SJcTz05F7\n2PFPVrfn5SphMZl4YUIQ3p4fDlNjAxyKLcD3B1MpPaBPE0oZVBtzDkZFuOLg5XwwINeNVvdiiQ5F\nagvHBZBdATKCwJ6zOZDKCCwa56+TIE5PEUtk+GyPvHrWycZYo3hRGwRBUCqdP3wxutPnmhsbYu28\ncJibGGL/hVyKlkBvgiAInL5VjH0XcmFubIB3no+gyJI+7DHP3C3BluPpYDIYeGN2GEZGqlS77hXW\n471td8jbC8f6Y+3ccPL29wdTSdlOCxND/P7uSL0Nfimt5uGT3fFIyK6Bv6sFNiztjwA3K+y7kIuf\nj92DRCrDkgmBWDYlGEYcNuKza/DR73cp4XAA8HSUe6EVHe7XRdkts7gBy76Kpeia64KjtTFYTAZa\n2kWYNdwbq2eHUSrX3//1DilDDMijRrOG++DkzWKcvSuXnX17fgQEamtXgJslpg2lbqb3nc+ltLcN\nDHZAK1+MErWJaLx2MTYd6lxRjc2Srw1+irSBen2AtTltvJ84Jh2MMUANm3dcpFUFayKwmAwYcTQX\n8cYWASzNDEmBFgbkYZbaJj64hiytI0S1UdKhv3tMPze8ObcvDNhMbD+dhUOx+Z16vGOj3bBptUoy\nU5lvnzHMC+4OpriRVomPd8Qhp/ThpVCDPK2xYWl/hPnYIKOoAR/viEN6kWaf6JPmmFIGNcYbd7Oq\nUV7bhiF9nDTU8eKyapCjkIMM8rBCP7X2neup95Ff0Yx+AXYI87HF42D5N7Hk37rKmL6ppp29ZW3M\nA59vb2mEN+f0BceQhV9PZSC7l0njEgSBQ1cKcPRaIWzMOXh/UVSPZ3FLZTL8cS4Hh64UwNLUEO8v\niqR850euyjerSj5YHEXKcUqkMizdeJnslx7TzxXf/2eoXmoKCIJAbHIFPtuTiJpGPiYO9MC65yMg\nkkjxvz8ScSmxHM62JvjwxX4Y1tcZUhmB/RdysfV4uoaRDfKwooz2nDzYE4ufk/eZJ3Wh6yBTFN6p\nF5Hpip+rvNXKhMvG2/PkfefKKIWMILB042WKd/vShECMH+COM3dKcOJGEWwtuFg3PxyGbCbe/1W1\ncXpzbl/K/4nPrkF8dg0c1JykWcN9cPRqIVhMBiL85N9lRV3X7WHKtT+ANN7qnveTE66ijbcCMmyu\nVmwi7GSiGKAm0tIuhqmxgcaPUiYj0NQqIr/cumYBLM04YDEZqG3iw97SSOcfcmm16uKys5QfL9TL\nBv9dHAUHa2OcuVOKzUfudepBmxsbYsd7ozB3pKoP+dj1IoT52GDyYA/Utwjw1f5k/HU5D2LJw/Vw\nmxsbYvXsMMwf5Ys2vhjf/ZWKQ1fyuxSKeZyUVvNwWyGDGuFnh2PXCmFowMSMGG/K8wQiiUZrmPJ7\namkT4XBsAbiGLCwY83iq7NUFI75aMUina+bglXwyzbJ+SbTObWAejmZYNbMPCAL46eg9lNd0vag9\nLchkBDYfSsXZuFI4Whvj/UVRPdaW5wsl+OFwGmJT7sPVzhT/XdyP3AzICAL/9+sdyoztTauHkjUf\ndc18ivDK6llheF5P170j3m8AACAASURBVAtfKMGvpzKx51wOOAZMrJkThtkjfJCQXYsNO+NRUs3D\nsDAnfPhiP7jYmcpTWvuSNLpVuIYsmHDZlPkF36wehpkx3ogMsAODAcqcBXUK77fglS+vkEW4umJl\nxoGDtTHyypvh52qBj5f0p4hEiSVSvPIlVXxl5fRQxPR1xsUEeeublRkH7yyIgJUZR6MNrb5FdT4t\nbSL8cS4HBmwmpR325r1K1DTxMSLCBd7O8hB9V143ADLq5kdWmtOe91OFqbawuajzWd7KnkweX6y1\n0rylXT6r28qMA6lM3uNtY8FFU6sIIolM55A5AJSped7qIVMnGxP8d3EUQjytkJJfh//tTexyxmxk\nBwGI07dKcPpWCVbN6AN7KyOciyvDJ7sSKCGl7sBkMDCuvzv+74Uo2Fsa4czdUmzcl/TE594CwOFY\nhQzqSB+cvVuK5jYRxvd318gd/327hLwGnuvvRqkR+OtyPtoEEsyI8dYp59xTSqp4pCLVonH+sLV4\n8DWTUdxAvub5MX7d9j5DPK3x8qQgxeCOFDLd87Qikcqw7WQGzt8tgbuDKd5bFKlRyNldGloE+GJv\nEtILGxDqbY331Y4pEEnwypdXKCIdv70zAuYKA5GSX0eZ/vfVikEI99NPhKa8phWf7E7A3cxq+LiY\nY/2S/ghwt8KuM1nYdjIDBIBXpwRjycQgcAxYSCuox7qtt1B4n1qwFe5rC4FISjoqoV7W+PnNGAR4\nyA2pubEhAtwskV/RTNbtAHKPf+PeRDKF0x2GhTmhjS9GdUM7xvd3xzqFAVbCaxdh+TfUKvW35oej\nX6A9rqXex/6LeQoBqwjYWhpRDLdShEW5mSAIAn+cz0ErX4wwHxvkq9UGHb9RBI4hC1MGe5LfWVdY\nmBqCL5TC3tKIPF+K593Da60n0MZbgVbjre55q4XNTbhsMBgMSKQy8IUS7ZXmLao2sUaeEDKCeKhi\nNRlBoFTNAxraoU/VhGuANXP7YnSUKypq2/Dp7oROQ+BZigK1ReP8MWu4yuPcfPQeooPsMSrSBRV1\nbfhsTwJO3yqGVPZwXrOXkzk+XhKNgSEOKLzfgvU74xCXpX083+Mgo7gB6UUNCPG0grONCc7FlcLC\n1BATBlAHiFQ3tpPelBGHRSlGyypuwO2MKng4mmF0pOaMb30jkcrIIidbCy5G6fA/W9pEpG65n6sF\nxjxkG9LAEEfMG+WLplYRvjuYolXz/2lAKJbipyP3EJ9dg2Ava7yzIFKnBbkrSqt5+GxPAspr5YMt\n3pgdRrbWNbQIsPI7ldHwdDTDjvdGkRKrBy7m4Ue1aVS/rhuh04brQRAEgeup9/HpngRUN7Tjuf5u\nePf5SAjEUny2OwHXUivhbm+K9S9FY2CII6QyGY5cLcCmQ6kax/J1tUCKmrb3a9NCsHZeOPkelUT6\nyzf6yXly77uiTj66M7dce5FsVwwJdcT1tEqwWAysmtFHo7OjuqGdUp8ByFMQIZ7WuJ1ehd1nsmFq\nZIC354fDwdoYW4+nQ6BwrNYviSZz9krjHZdVg8ScWvg4m6NKMUhIXZ9hwgB3mJsY6iSqZG3GAV8o\ngZ+bPKoiIwiK0uWTNN50q5gCEy3V5hTjrRZ6NFUsEF1XmssvJGtzbo+K1Wqb+OSFCsgnf3WExWRi\n4Vh/uNiaYN+FXHzzZwpeeC5Ao7CJ1DP3sIKTjQmG9nEic6Onb8mN1iuTg3A4Vp47TM2vwyuTgx8q\nBGnEYWPZ5GCEeFpj7/lc/HIiA5nFDVgw5vEUeSlRl0GdPcIXR68VQiSRYWGMt4Y07YGLqsEji8YF\nkAuaWCLDnvO5YDCAF8cHgMl89L3Q6mHXja8NeuDzZQSBNT+pFsD3F0X16P8/198dTa1CnIsrww+H\nU/H2/IjH+r09iHaBBD8eTkVueTNCva3x8bJB4DX3LMKTVlCHrcczIBRLMXekL57r70amKXLLmrBx\nXxL53FnDvUk1PoIgsHbzTbJQM9TbmlK01hOEIin+OJ+DW+lVMOaw8drUEIT72eLGvUrsO58LkUSG\n0ZGumDvKBwZsufDO1hMZGiM8vZzMUFTJQ77C+LKYDHz52qBOjU+kvx32X8xDYk4tckqbEP8QRYwj\nwp1RWNmCm4p01coZoRrKbHnlTaToECBfN95fFAlXO1MkZNdg+9+ZMOKw8da8cLjYmeJSYjl5L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WvC9fvozRo0cDAEJCQtDS0gK5nO4khUIhhEIh2traoNPpoFQq4eT098dJmhu1sPfcnM6biQOl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19HFk8Jw9XRiooaFU8bcQ06Ay8ujKZnC3awMUlFrNt/hTqllomDA5gx1P+6mf/moNMJJvv9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\nupSHW+lVqGsWQyxVYl6sr8Eca0NoapNS0yUXOzMsnmI4e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4J0L5IeHh7/usYbAKWBpsPK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eq0CfHkctc+Gdx8Zh+UzDgiAoSOVK/Hi0CDtOlMDGio/1S+Jx/9RRtz1UhEJLp4jlnLd1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\nCAQd4/8BQi5Jynco8B0AAAAASUVORK5CYII=\n","text/plain":["<matplotlib.figure.Figure at 0x7f411cf55860>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"9Xx00Y8Rf_8p","colab_type":"code","outputId":"396af8bf-cbea-44d8-9814-c6a2d5e8a582","executionInfo":{"status":"ok","timestamp":1547540103364,"user_tz":-480,"elapsed":1064,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":361}},"cell_type":"code","source":["fig = plt.figure()\n","ax1 = plt.subplot()\n","ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n","ax1.scatter(df[\"probability\"],df[\"ytrue\"],s=10)\n","ax1.set_ylabel(\"True label\")\n","ax1.set_xlabel(\"predcited probability\")\n","ax1.set_ylim([-0.05, 1.05])\n","ax1.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe8AAAFYCAYAAAB6RnQAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xt8zvX/x/HHtfPhmtm0GWPOwpxy\nKKeQJinpNDaFDr7Jt3QQOaVGDlEkIUkqqUTatyOJpHIoRc4Jw4xmNuYwmx2v3x/7uTJ2Yrv2ua7t\neb/d3G7XdX1Oz8/bZ5/X9f58PtfnY7JYLBZERETEYTgZHUBERESujoq3iIiIg1HxFhERcTAq3iIi\nIg5GxVtERMTBqHiLiIg4GBejAxRXYuI5m8zXz8+L5ORUm8y7olAblozar+TUhiWj9is5W7VhQIBP\nvp9X+J63i4uz0REcntqwZNR+Jac2LBm1X8mVdRtW+OItIiLiaFS8RUREHIyKt4iIiINR8RYREXEw\nKt4iIiIORsVbRETEwah4i4iIOBgVbxEREQdj0+K9b98+wsLC+Oijj64YtnHjRsLDw4mIiGDu3Lm2\njCEiIlKu2Oz2qKmpqUycOJH27dvnO3zSpEksXLiQqlWr0r9/f3r06EH9+vVtFSdf67cf4+PVf1Mz\nwIcGIb7UD/ajXrAvscfPgclCwxp+uLs5k56RzeHjZ6lWxZvYhLNgMdGwZmUA9sWdzjMukGf8+JPn\nqR1UyToMIPF0Gqs2H6HHjSEEVPa0jl87qBKA9fWl0xRHekY2+44mW/Nd7fQFzfNa8ziakqzr5dMW\nNa/0jGzrttOhkmepZilNJc1hL+txuavJZat1sHXbFGf+F/cZvidTqerjXuC2Wlb/h4Utqzh/Y2fP\nZ7BhZzxV/T0JrV0FoMB9cXH2u5ePs+fwKQ4cO01KWgZdW9Zg+4Ektuw/wbHENOs0743uZrsGuoTN\nirebmxsLFixgwYIFVwyLi4vD19eXatWqAdClSxc2bdpUpsV7/fZjvLfybwAOxJ/jQPw54CiuLk5k\nZuUAUKuqmWF9WzJr+XYOxZ/D1cVEZpYFgJBAMwBHTqRYxx39YGsAXl2ylUPx53BzdSIjM4c61XwY\n2a8V7m7OJJ5OY/Tbm7AAP249xsuDbuS9FX9xKP4ctYN8sFgsxCak5JmmONIzspn68RZiE3Lz1A7y\nYdQDxZ++oHleXJerzeNoSrKul0/7THgL6zaT37zSM7KZ9slWDh/PvV//VxtiGRHRMs9OxR7avaQ5\n7GU9SpLLVutg67YpzvyLs88oy//DwpZVnL+x9MxsRr69kYzM3P13cIAXrs7OHD5+5b4Y/t1PF7Tf\nvXSZtaqaybFA3P/v7wF+2ZGQ73o8OnVtmRRwmxVvFxcXXFzyn31iYiL+/v7W9/7+/sTFxRU6Pz8/\nr1K9d+zHa/7O9/OLhRsgNiGFPXFnOBR/7v+HWazDjlzyn3hx3DPp2VgsFuv4FzeiQ/HnOJOeTWhw\nZaLXH+TiXCzAys1HrONf3JlfPk1x7IpJsv4RXpzX1Uxf0DwvZisqT0E3z3cUV7OuRU27J+50ofPa\nFZOU5/865tiZPOOUJEtpKmmOsl6P4m6DV5PLVutg67YpzvyLs88oy//DwpZ15d/YmSvGjTl22rrP\nBTiW+O9DQi7fF1+6ny5ov3vpMi9tp0ulnUvE0yfgis/LYn/oME8VK+2ntTwYdr21532py3veTWr6\nUqeaT7F63r7uuV8uLo5/6bc9X3dnEhPPcXPTany7/jAWwAT0vDGE+KTz+X4DvDhNcVT2cKFWVXOe\nb9FXM31B87y4LoXlCQjwsdlT38pKcde1ONM2qVm50HlV9nChdpCPdadRL9g3zzglyVKaSpqjLNfj\narbBq8llq3WwddsUZ/7F2WeU5f9hYcu68m/M94pxQ2tWtu5zoeCe9+X76YL2u5cuM7+e97G9P7N9\n1Zu07DmM6g075lmX0myjgr4ImCwWiyXfIaVk9uzZ+Pn50b9/f+tnR48eZfjw4SxduhSAOXPmULly\n5TzjXM4WG8z67cf4ZE3uOe+GIb7UrZ57zvtIQgqYcmgQ/O8579iEswT5e3PkxFmwONGghi8A+4+e\nyTMukGf846fOU6vqlee8V/9+hO5t/z3nHZtwllpVc8+rXHx9Ledd9x9LtuYrrcN7ReUpD8Ubireu\nxZ22qHmlZ2Rbt532LWpy7mxaofMzSklzlNV6XO02eDW5bLUOtm6b4sz/4j7Dt5IXgYWc8y6rbbGw\nZRXnb+zs+Qw27jxO1SoeNKmVe867oH1xcfa7l4+z5/ApYo6dISU9gzq+Gfz3sX607TkE5yrNrNOU\n9iFzuyreAHfeeSfz588nKCiIiIgIpk+fTp06dQqcjz18Y5f8qQ1LRu1XcmrDklH7FS0nJ4d3332b\n227rSe3aubUqKyvLenrYVm1YUPG22WHzXbt2MW3aNI4dO4aLiwurVq2iW7du1KhRg+7duzN+/HiG\nDx8OwB133FFo4RYRETHSunU/MG7caDZu3MAHH3wMUOB1XWXB5j3v0qKet/1SG5aM2q/k1IYlo/bL\nX05ODjk5Obi4uGCxWJg3bw59+kQSEJD/RWpl2fPWHdZEREQuk5CQQJ8+9/Dqq1MAMJlMPPHEU/kW\nbiM4zNXmIiIiZcXb24vY2EN4e3uTk5ODk5N99XVVvEVERID4+H+Ij/+HVq3aYDb78O23qwkMrIrJ\nZDI62hVUvEVEpMJLSUnh1ls74erqxvr1m/HxqUTVqkFGxyqQireIiFR4ZrOZJ598FrPZjNls/3eM\nVPEWEZEK6YsvPmfNmu+ZPfttTCYTTz75tNGRis2+zsCLiIiUAYvFwkcffcjXX3/B/v37jI5z1dTz\nFhGRCuPw4UPUrl0Hk8nErFlzSU+/QN26Zfs46tKgnreIiFQIL744hptvvpG//94LQHBwDYcs3KDi\nLSIiFUSHDp1o0iQUZ2fjnytfUireIiJSLp09e4bJkyeQmpr7SOmePe9k5cq11K/fwOBkJafiLSIi\n5dL8+W8xa9YM3n13vvUze7tT2rXSBWsiIlJuXLhwAQ8PDwCGDn0WHx8fHn10sMGpSl/5+AoiIiIV\n3tatf9Cp0418++3XAHh6ejJkyFDc3NwMTlb6VLxFRKRc8PGpRFJSIgcPxhgdxeZ02FxERBzW5s2/\nERQUREhILRo0aMiWLbuoUqWK0bFsTj1vERFxSDt2bKN37x4MG/YUFosFoEIUblDPW0REHFSzZi14\n9NHH6N37Prt8bKctqXiLiIhDyMjIYMaMqbi5uTN8+ChMJhNTprxmdCxD6LC5iIg4hPT0Cyxb9ilL\nl37ChQsXjI5jKPW8RUTEbmVmZnL0aBx16tTFx6cSH3/8GTVr1rT+lruiUvEWERG7lJWVRe/et5OQ\ncJyfftqEj08lmjQJNTqWXVDxFhERu+Ti4kKXLrdw7NhR69XkkkvFW0RE7MaBA/tZvvxTRo0ah8lk\nYtSoFyrcleTFoQvWRETEbrz44mhef/01Nm3aAKDCXQD1vEVExFCpqal4eXkBMHXqDHbs2EaHDp0M\nTmXf1PMWERHDLF36Ca1bh3LgwH4AatWqzV133WNwKvun4i0iIobx8vImOzubI0dijY7iUFS8RUSk\nzFgsFqKjPyMtLQ2Au+66m82bt9OtW5jByRyLireIiJSZRYveY8iQQcyYMc36WeXKfgYmcky6YE1E\nRMpMnz6R7NixjUce+Y/RURyaet4iImIzJ06cYODAfnz33QoAvL29ef312QQH1zA4mWNTz1tERGzm\n1KmTrF27Gk9PD26//Q6j45QbKt4iIlKqkpKSyMrKJCioGo0aNWbFijU0bdrc6Fjlig6bi4hIqTl6\nNI7OnW9i6NAh1vuRN2/eEicnlZvSpJ63iIiUmuDgGrRr14E2bW7EYrHo9qY2ouItIiIlsnr1dxw5\nEsugQY9jMplYuPBDFW0bU/EWEZFrlpaWxvPPD+P06dPce284/v5VVLjLgIq3iIhctZSUc5jNPnh6\nevL22wvx8amEv38Vo2NVGLqCQEREis1isTBmzAi6du1ISso5ANq160BoaFODk1UsKt4iIlJsJpMJ\ns9kHb28vEhMTjY5TYal4i4hIoVJTU/nkk8XWn36NGDGa77//iTp16hqcrOLSOW8RESnUyJHDWLZs\nCb6+lbnzzrtwd3c3OlKFp+ItIiJXuPQ32sOHjyIwsKoe22lHdNhcRETy2LZtKz16dOXgwQMA1KlT\nl5deehlPT0+Dk8lFNu15T5kyhe3bt2MymRg7dizNm/97b9uPP/6Yr776CicnJ5o2bcoLL7xgyygi\nIlJMsbGH2bbtT9auXUPduvWNjiP5sFnx3rx5M7GxsSxdupSYmBjGjh3L0qVLAUhJSWHhwoV8//33\nuLi48Oijj7Jt2zZatmxpqzgiIlKIPXt2U7duPTw8PLj77vto0OB6mjQJNTqWFMBmh803bdpEWFju\n+ZF69epx5swZUlJSAHB1dcXV1ZXU1FSysrJIS0vD19fXVlFERKQQK1euJCzsZl577RXrZyrc9s1m\nxTspKQk/Pz/re39/f+tvAt3d3XnyyScJCwvjlltuoUWLFtSpU8dWUUREpBA333wzN97Yjo4dOxkd\nRYqpzK42v/j7QMg9bD5//ny+++47zGYzDz30EHv37qVRo0YFTu/n54WLi7NNsgUE+NhkvhWJ2rBk\n1H4lpzYsvuzsbGbMmEGzZs3o2bMnABs2/GJwKsdXl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ZEjsSxatJCcnBwqV/YDwMPD84qH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\n3iIiIh6j5C0iIuIxL6NFX9+mWAUIAAAAAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure at 0x7f411a60c940>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"8X3p-BGZlI-J","colab_type":"text"},"cell_type":"markdown","source":["### 使用可靠性曲线的类在贝叶斯上绘制一条校准曲线"]},{"metadata":{"id":"cBlA68qCf_8s","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.calibration import calibration_curve"],"execution_count":0,"outputs":[]},{"metadata":{"id":"rhBmD-qJf_8t","colab_type":"code","colab":{}},"cell_type":"code","source":["#从类calibiration_curve中获取横坐标和纵坐标\n","trueproba, predproba = calibration_curve(Ytest, prob_pos\n","                                         ,n_bins=10 #输入希望分箱的个数\n","                                        )"],"execution_count":0,"outputs":[]},{"metadata":{"id":"0TxMOMe1f_8u","colab_type":"code","outputId":"40296da6-1d55-4a2c-bf02-908c2e826c0e","executionInfo":{"status":"ok","timestamp":1547540192943,"user_tz":-480,"elapsed":857,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["trueproba.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(10,)"]},"metadata":{"tags":[]},"execution_count":83}]},{"metadata":{"id":"UHkBo6Dqf_8v","colab_type":"code","outputId":"73910453-de9e-42c3-dda7-b223c433a6c8","executionInfo":{"status":"ok","timestamp":1547540196151,"user_tz":-480,"elapsed":1285,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":361}},"cell_type":"code","source":["fig = plt.figure()\n","ax1 = plt.subplot()\n","ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n","ax1.plot(predproba, trueproba,\"s-\",label=\"%s (%1.3f)\" % (\"Bayes\", clf_score))\n","ax1.set_ylabel(\"True probability for class 1\")\n","ax1.set_xlabel(\"Mean predcited probability\")\n","ax1.set_ylim([-0.05, 1.05])\n","ax1.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe8AAAFYCAYAAAB6RnQAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noqN7Ex5en+zsLNLS0tizZyfR0b35449YLl+O55lni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MjJZ8A9YVWyBWppzBbvRYsW0blz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\nRs1bRETExfw/Cu1vO15gekoAAAAASUVORK5CYII=\n","text/plain":["<matplotlib.figure.Figure at 0x7f411a5fc6d8>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"1IPjmvWllkJa","colab_type":"text"},"cell_type":"markdown","source":["不同的n_bins取值下曲线如何改变"]},{"metadata":{"id":"Xxe_8LWRf_8x","colab_type":"code","outputId":"2a5d7e98-8ba0-42d2-b756-64ca5aa2b5ac","executionInfo":{"status":"ok","timestamp":1547540227371,"user_tz":-480,"elapsed":1440,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":285}},"cell_type":"code","source":["fig, axes = plt.subplots(1,3,figsize=(18,4))\n","for ind,i in enumerate([3,10,100]):\n","    ax = axes[ind]\n","    ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n","    trueproba, predproba = calibration_curve(Ytest, prob_pos,n_bins=i)\n","    ax.plot(predproba, trueproba,\"s-\",label=\"n_bins = {}\".format(i))\n","    ax1.set_ylabel(\"True probability for class 1\")\n","    ax1.set_xlabel(\"Mean predcited probability\")\n","    ax1.set_ylim([-0.05, 1.05])\n","    ax.legend()\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABA8AAAD4CAYAAACDmM9pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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18fAgJq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\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Ii2q/TkTEUdJreQzgpfTqEMr+o5mcSM3nTFEpfj6etbpfZk7FSQv1e1e/QmB4a0JCQklK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\nrkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7f4118dbb3c8>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"5dvkdGycf_8z","colab_type":"code","colab":{}},"cell_type":"code","source":["name = [\"GaussianBayes\",\"Logistic\",\"SVC\"]\n","\n","gnb = GaussianNB()\n","logi = LR(C=1., solver='lbfgs',max_iter=3000,multi_class=\"auto\")\n","svc = SVC(kernel = \"linear\",gamma=1) #置信度"],"execution_count":0,"outputs":[]},{"metadata":{"id":"q4Jy0tEFf_8z","colab_type":"code","outputId":"bca13231-0d3c-4ca9-cc91-0c9595a47cb4","executionInfo":{"status":"ok","timestamp":1547540269756,"user_tz":-480,"elapsed":2809,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":403}},"cell_type":"code","source":["fig, ax1 = plt.subplots(figsize=(8,6))\n","ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n","\n","for clf, name_ in zip([gnb,logi,svc],name):\n","    clf.fit(Xtrain,Ytrain)\n","    y_pred = clf.predict(Xtest)\n","    #hasattr(obj,name)：查看一个类obj中是否存在名字为name的接口，存在则返回True\n","    if hasattr(clf, \"predict_proba\"):\n","        prob_pos = clf.predict_proba(Xtest)[:,1]\n","    else:  # use decision function\n","        prob_pos = clf.decision_function(Xtest)\n","        prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())\n","    #返回布里尔分数\n","    clf_score = brier_score_loss(Ytest, prob_pos, pos_label=y.max())\n","    trueproba, predproba = calibration_curve(Ytest, prob_pos,n_bins=10)\n","    ax1.plot(predproba, trueproba,\"s-\",label=\"%s (%1.3f)\" % (name_, clf_score))\n","    \n","ax1.set_ylabel(\"True probability for class 1\")\n","ax1.set_xlabel(\"Mean predcited probability\")\n","ax1.set_ylim([-0.05, 1.05])\n","ax1.legend()\n","ax1.set_title('Calibration plots  (reliability curve)')\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe8AAAGCCAYAAADJ40tJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmpry+eezSEhIIDExkQ8//Ij69V20r+XLL+fSsaMHAM+fP2XatA948SKE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eS8Z1/q1jpk+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\n","text/plain":["<matplotlib.figure.Figure at 0x7f4118da3358>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"9_WfhaO5luuF","colab_type":"text"},"cell_type":"markdown","source":["## 4 预测概率的直方图"]},{"metadata":{"id":"kIbkbslyf_85","colab_type":"code","outputId":"30886a40-aff5-49a2-c770-5eacc9ca2414","executionInfo":{"status":"ok","timestamp":1547540305344,"user_tz":-480,"elapsed":2561,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":388}},"cell_type":"code","source":["fig, ax2 = plt.subplots(figsize=(8,6))\n","\n","for clf, name_ in zip([gnb,logi,svc],name):\n","    clf.fit(Xtrain,Ytrain)\n","    y_pred = clf.predict(Xtest)\n","    #hasattr(obj,name)：查看一个类obj中是否存在名字为name的接口，存在则返回True\n","    if hasattr(clf, \"predict_proba\"):\n","        prob_pos = clf.predict_proba(Xtest)[:,1]\n","    else:  # use decision function\n","        prob_pos = clf.decision_function(Xtest)\n","        prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())\n","    ax2.hist(prob_pos #预测概率\n","             ,bins=10\n","             ,label=name_\n","             ,histtype=\"step\" #设置直方图为透明\n","             ,lw=2 #设置直方图每个柱子描边的粗细\n","            )\n","    \n","ax2.set_ylabel(\"Distribution of probability\")\n","ax2.set_xlabel(\"Mean predicted probability\")\n","ax2.set_xlim([-0.05, 1.05])\n","ax2.set_xticks([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1])\n","ax2.legend(loc=9)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAf0AAAFzCAYAAAA0dtAgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Xt8zvXj//HHtZM5bJg2cswhfIQ5\n5TARsj5LCp+cttBBSSHJoTXnIptzRVFyaAw5VCohH4cSVrbqg9JWISS22WQHdrp+f/jt+lozF7oO\n77me99vNre39fl/v67lL9rxe7+v9fr1NZrPZjIiIiNzy3JwdQERERBxDpS8iIuIiVPoiIiIuQqUv\nIiLiIlT6IiIiLkKlLyIi4iI8nB3A3pKSLth8nxUrliE1NdPm+7UVo+cD42c0ej5QRlswej4wfkaj\n5wPjZ7RHPn9/n6su10j/Jnh4uDs7wjUZPR8YP6PR84Ey2oLR84HxMxo9Hxg/oyPzqfRFRERchEpf\nRETERaj0RUREXIRKX0RExEWo9EVERFyESl9ERMRFqPRFRERcxC0/OY9ISfRk5A677HdpeJdrrj95\n8gRvvjmXc+fOAVClyu2MHh1OhQoVbJ4lOno5zZu3oHHjptf9mNOn/2DQoP40aNAQk8lEdnY2zz03\nksDAZjbPJ3IrUumLCAB5eXmMHz+OF198yVKiK1cuZ/78WUyZMt3mzzdw4OM39biaNWuxYME7AHz/\nfTwrVixh7twFNkwmcutS6YsY2LVG5v7+Ptc9zfT1HDn49ttY6tSpW2jUHBY2CLPZTGJiAnPnRuHh\n4YGbmxuvvhpJRkYGEya8xHvvRQMwePBApk2L4sSJ33n33bcoVcqbKlUCCA+fQnz8AcuyihX9mDx5\nGlFR0+jU6T6aNWvO1KkTyMrK4uLFi4waNZZGjRrTr19PevT4D19//RXZ2dm8/vpbRTKfO3eO227z\nB7hqxpUrV1CzZk26d+8JwIABfVi48F22b9/G9u1b8PLypG3bDoSGDiAh4Qhz5kTh6emJl5cXU6fO\nwMfn6lOZipRUKn0RAeD3349Rp069Qsvc3C6f9pOWdo5Ro8ZSv35DlixZxLZtn9O+fcer7mfDhrUM\nHz6KwMDmfPfdPs6fTyu0bPfuHZw/n2bZPiUlhe7de9KxYyfi4r5l1aoVTJ8+i7y8PGrWvIOwsEFM\nnvwyBw58y5131uf3348zfPgQsrOzSU5OYs6cN4vNGBLSjTffnEf37j05evQ3qlatRkZGBrt2/Ze3\n3noPf38fevfuS+fOXdm8+RN69epNSMiDxMV9y7lzKSp9ueWo9EUEAJPJjby8XMv34eEvkp6eTlLS\nWaZPn8Xbb7/JpUsXSU5OIjg4pNj9dO7clVmzZnD//SH07fsfvL0rFFrWteu/qVTpNsv2fn6VWLFi\nCatXR5OTk4O3t7dlXWBgcwD8/SuTkZEOFD68f/z4MSZOfImlS1dRsWKlIhnr1KlHevoFUlNT2bNn\nN8HBIfz002FOnjzBiBHP4OXlQWZmBn/++Qf33HMvs2dHcuLE79x3XzC1at1hy5dXxBBU+iICQO3a\ndVi/fo3l+8jIuQD07v0Qr78+m0cffYy2bYOIiYkmKysTk8lU6PG5uZffMISEPEibNu348stdPPvs\ns0yZMqPQspdeGsW0aTMtj/vggxhuuy2AiRNf5ciRH1mwYL5lnbv7/92IxGw2F8lcq9YdlCpVirNn\nz1w1I0BwcAi7d+/gwIFviYqaS2zsPtq1a8+4ceOLfESyZMn77N37FdOmTWH48Bdo0aLVzb+gYgj2\nOinW1qydZGsrumRPRABo2fJuzp49w549X1qW/fzzETIzM0lKOku1atXJzs5m//6vyc3NpUyZsqSm\nnsNsNpOSkswff5wEYPnyJbi7e9Cjx3/o1q0bx479VmjZfffdz7Fjv1me4/z5NKpVqw7A7t07LW8e\nrsdff50nJSUFf/8Ay36uzAjQteu/2bz5E267rRLe3t40aPAv4uPjuHjxImazmfnzZ3Pp0kU2bFjL\nX3+d5/77H6BfvzASEo7Y4mUVMRSN9EUMzJGjFJPJxJw5bzJ37kyWL1+Cp6cH3t6liYqay2+//crL\nL4+hWrVqPPJIP+bNm0mXLsG0atWap54aRL16d3LnnQ0AqFy5Ci+88Bw+Pr7cdltFHnqoD5mZmZZl\nPj4+9O8/wPLmIiTkQaZNm8zOndt55JG+bN++jc8+21RszoLP9AGys7MZNWosnp6ePPJIv6tmvPPO\n+pQuXYauXS9/JFGlShX69g1l2LCnKVXKk3btOlCqlDfVqtVg4sRwypUrh6enJxERk+38iosjOWok\nfaMcfSTCZL7aMbNbyPWe3XwjbuSsaWcwej4wfkZn53PWdfq25uzXESAtLY3Ro0fw7rsrLCcmFjBC\nPmuMntHo+Qr+LRm99G2dz9//6iehaqQvYkDX8wvA6L9sjeDLL3fx3nuLGTFiVJHCF3FFKn0RuWV1\n7NiJjh07OTuGiGHora+IiIiLUOmLiIi4CJW+iIiIi1Dpi4iIuAidyCdiQMN2jLPLfhd2mVnsutOn\n/yh0A50b9frrc+jTpz9Vq1Yrsi4jI53Dhw/RunXbm7qlrojYhkpfRGxi5MjRxa77+ecjfPPNflq3\nbnvTt9QVkX9OpS9iYNcamd/Idfo3e+Tg119/Ye7cKEwmE2XKlGXChCmUKVOWV16ZyJ9/nqZJk6bs\n2LGdDz/czPDhQ3jxxXHk5uZablFbrlwZxo9/lblzZ5KZmUGNGjU5dOh/dOp0H23atGPatMmcOXMa\nL69STJgwFX//gJvKKSLXR5/pi0ixXn99Ns89N5IFC96hWbMWrFu3hv3795KdfYl33llOixZ3k5yc\nVOgxBbeoXbDgHZ566inOnUshLGwgXboE06PHfyzbff75p1SqVIm3317KQw/1LDTnv4jYh0pfRIp1\n7NhR7rqrMQAtWrQiIeEIx48fpUmTQADatWtf6E54APfccy/Ll7/Hu+++TaVKlYq9Re3PPx+x7Kdr\n13/Tq1dv+/0gIgLo8P4N020axVXl5ubg5uaG2WzGze1y0ZtMpiK32G3VqrXlFrXh4eE888yIq+7P\n3d2N/Pxb+tYfIoajkb6IFKt27bocOvQ/AL77Lp4GDf5FtWrV+fnnHwH45pv95OXlFXrMlbeofeyx\nx0hIOILJZCqyXcOGjYiP/xaAr7/+ivffX+qAn0jEtWmkf5OMOpIuKUci5PrY69K94lx521qAp54a\nyuLFCzGZTPj4+BARMRkPD08++2wTzz47mObNW+LrW77QPq68RW3ZsqUZM2Y8aWmpLFr0ZqET9bp2\n/TcHDnzD8OFDcHf3YMKEKY76MUVclkpfRAC4/faqfPFF0ZPp3nxzcaHv//rrPN2796BTp/tISjrL\nrl3/BWDBgncAqFOnHm3bBgH/d4WBn18lPv54a5F9T5z4iq1/DBG5BpW+iAFd61K9As66tW6ZMmXZ\nsWM7MTHRmM35jBjxosMziMjNUemLyA3x8PDglVdmODuGiNwEncgnIiLiIlT6IiIiLkKlLyIi4iJU\n+iIiIi5CJ/KJGFDCU49b3+Ym9lt/yfJrrt+w4QO2bt2Ml5cXly5d5MEHe/Dhh+tYsWKNZRuz2Uzv\n3g+xZMn7eHuX5o035vLzzz/i5VUKX19fRo8Op3LlKjeRTkTsTaUvIgCcPv0Hn3zyEUuWvI+Hhwcn\nTvxOVNQ0PDw8OXbsKHfcURuA//3ve2rVuoOKFf2IiprO7bffzksvjQdgx47tTJkSwdtva3Y9ESNS\n6YsY2LVG5jdynf71HDlIT08nO/sSOTk5eHh4UKNGTRYseIfVq1fy3/9uY/DgZwDYseMLgoNDyMzM\n4Jtv9vHBBx9b9tGlS1fuvrvNdWUSEcfTZ/oiAsCdd9bnX/+6iz59Hmb69Cn8979fkJubS9eu91tm\n3cvPz2ffvq+5997OnDp1kpo1axW5y56Pj48z4ovIddBIX0QsJk58hWPHjvLNN/uIiXmfjz5azxtv\nLKJChYr8+usv/PXXeerXb0iZMmUBE/n5+c6OLCI3QKUvIsDlE/Sys7O5447a3HFHbR55pB+PPtqb\nM2f+JDg4hJ07t3Phwl8EB4cAUK1aNY4fP0Z2djZeXl6W/Rw58iMNGzZy1o8hItegw/siAsCnn37M\nzJnTMZsv3+M+IyOd/Px8KlasSKdO9/Htt7H88MP3tGvXHrg8B/8999zLkiVvW/axa9d/WbBgvmUf\nImIsGumLGNi1TsC7mUv2rqVbt4c4fvwYQ4Y8RunSZcjNzeWFF8ZSqpQ3pUp54+fnh69v+UKj+pEj\nR/PWW28waFA/fHx8CQiozGuvzcJkMtk4nYjYgkpfRABwd3dn+PAXil0/Y8acIss8PT0ZOXK0PWOJ\niA2p9EUMyNokOuC8W+uKSMmlz/RFRERchEpfRETERaj0RUREXIRKX0RExEXYtfQvXrxI165d2bhx\nI6dPn2bgwIGEhYUxcuRIsrOzAdi0aROPPPIIffr0Yd26dQDk5OQwevRoQkNDGTBgACdOnADgyJEj\n9O/fn/79+zN58mR7RhcREbnl2LX03377bcqXLw/AG2+8QVhYGDExMdSqVYv169eTmZnJwoULWb58\nOdHR0axYsYK0tDQ+/fRTfH19Wb16NUOHDmXOnMuXCk2fPp2IiAjW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dMJTjzs7gojDWS39\n3r170759ew4fPozJZGLSpElUrlzZEdlERETEhoot/d27d3Pvvfeyfv36Qsu/+uor4PKbARFxLqOO\nogv4+/uQlHTB2TFE5P8rtvR//vln7r33XuLi4q66XqUvIiJSshRb+kOGDAHgnnvu4cEHHyy0bvXq\n1fZNJSIiIjZXbOn/9NNPHDp0iKVLlxY6ez83N5eFCxcSGhrqkIAiIiJiG8WWvpeXFykpKVy4cKHQ\nIX6TycS4ceMcEk5ERERsp9jSr1u3LnXr1qVt27Y0a9bMkZlERETEDqxesjdz5kxMJlOR5atWrbK6\n85kzZxIXF0dubi7PPPMMTZo0Ydy4ceTl5eHv78+sWbPw8vJi06ZNrFixAjc3N/r27UufPn3Iyckh\nPDycP/74A3d3d2bMmEGNGjU4cuQIU6ZMAaBBgwZMnTr1xn9qERERF2S19F944QXL1zk5Oezfv58y\nZcpY3fH+/ftJTExk7dq1pKam0qtXL9q1a0dYWBgPPPAAc+fOZf369fTs2ZOFCxeyfv16PD096d27\nN8HBwezcuRNfX1/mzJnDnj17mDNnDvPnz2f69OlERETQtGlTRo8ebbm0UETkZhh9kh5NFSy2ZLX0\nW7duXej79u3b8/TTT1vd8d13303Tpk0B8PX1JSsri9jYWMvIvHPnzixdupTatWvTpEkTfHx8AGjR\nogXx8fHs27ePnj17AhAUFERERATZ2dmcOnXKst/OnTuzb98+lb6IiMh1sFr6J06cKPT96dOnOXr0\nqNUdu7u7W44IrF+/no4dO7Jnzx68vLwAqFSpEklJSSQnJ+Pn52d5nJ+fX5Hlbm5umEwmkpOT8fX1\ntWxbsI9rqVixDB4e7lbz3ih/fx+b79OWjJ4PjJ/RyPkKpuA1csYCRs3o//EGZ0ew6usejwDGfQ0L\nGD0fGD+jo/JZLf3HHnvM8rXJZKJcuXIMHz78up9g+/btrF+/nqVLl3L//fdblpvN5qtufyPLi9v2\nSqmpmdeZ9MYYfZYxo+cz+kxtRs9XwOgZjf46Gj1fASNn1GtoG7bOV9ybCKulv2PHjpt+0q+++opF\nixaxZMkSfHx8KFOmDBcvXsTb25szZ84QEBBAQEAAycnJlsecPXuWZs2aERAQQFJSEg0bNiQnJwez\n2Yy/vz9paWmWbQv2ISIiItZZvbXuL7/8wsiRI3nwwQfp3r07L774Ir/99pvVHV+4cIGZM2eyePFi\nKlSoAFz+bH7r1q0AbNu2jQ4dOhAYGMjBgwf566+/yMjIID4+nlatWtG+fXu2bNkCwM6dO2nTpg2e\nnp7UqVOHAwcOFNqHiIiIWGd1pD9u3DjCwsJ4/vnnAYiLi2Ps2LFs2HDtz8M2b95MampqobP/IyMj\nmTBhAmvXrqVq1ar07NkTT09PRo8ezeDBgzGZTAwbNgwfHx+6devG3r17CQ0NxcvLi8jISAAiIiKY\nNGkS+fn5BAYGEhQU9E9+fhEREZdhtfTLli1b6OY6devWtYzWr6Vfv37069evyPJly5YVWRYSEkJI\nSEihZQXX5v9dvXr1iImJsfqtZWxXAAAZwUlEQVT8IiIiUlixh/fz8/PJz8+nXbt2bNu2jfT0dDIy\nMti+fTt33323IzOKiIiIDRQ70m/UqBEmk+mqZ8h7eHgwdOhQuwYTERER2yq29I8cOeLIHCIiImJn\nxZb+hg0beOSRR3j99devun7kyJF2CyUiIiK2V2zpu7ld/rjf3d32s9mJiIiI4xVb+r169QLg9ttv\n55FHHnFYIBEREbEPq5PzfPHFF1y4YOzpC0VERMQ6q9fpX7x4kS5dulC7dm08PT0ty1etWmXXYCIi\nImJbVkv/ueeec0QOERERsTOrpb9x40bLFLgFBg8eTOvWre0WSkRERGyv2NLftGkTa9asITExkUcf\nfdSyPCcnh5SUFIeEExEREdsptvQffvhh2rRpw5gxYxgxYoRluZubG/Xq1XNIOBEREbGda569X7ly\nZd577z1q1qxJ69at8fX15eTJk5QqVcpR+URERMRGrH6m//LLLxMcHEzz5s0ZMWIEwcHB7Ny5s9iZ\n+kRKuoSnHifB2SFEROzA6nX6Z86cISQkhM2bNxMWFsa4ceM4f/68I7KJiIiIDVkd6WdnZ2M2m/ni\niy+YPn06AJmZmXYPJuJs9Zcsd3aEa/L39yEpSRNnicj1szrSb926NS1btsTf35/atWuzfPlyateu\n7YhsIiIiYkNWR/pjxoxhyJAh+Pr6AtC1a1cGDBhg92AiIiJiW8WW/uLFi3nmmWcYO3YsJpOpyPqZ\nM2faNZiIiIjYVrGl36hRIwCCgoIcFkZERETsp9jS79ChAwBNmzYlISEBd3d3GjVqRPXq1R0WTkRE\nRGyn2NK/ePEio0eP5siRI9x1112kp6fz008/cc899zB9+nS8vLwcmVNERET+oWLP3l+4cCGVK1dm\n69atvPHGGyxdupQdO3ZQqlQp5s2b58iMIiIiYgPFln5cXBzh4eF4ePzfwYDSpUszefJk9uzZ45Bw\nIiIiYjvFlr67u/tVD+F7enpaLt8TERGRkqPY0r/aZXoF3N3d7RJGRERE7KfYE/m+++47OnXqVGS5\n2WwmNTXVnplERETEDoot/S1btjgyh4iIiNhZsaVfrVo1R+YQERERO7N6wx0RERG5Naj0RUREXIRK\nX0RExEWo9EVERFyESl9ERMRFqPRFRERchEpfRETERaj0RUREXIRKX0RExEWo9EVERFyESl9ERMRF\nqPRFRERchEpfRETERaj0RUREXIRKX0RExEWo9EVERFyESl9ERMRFqPRFRERchEpfRETERaj0RURE\nXIRKX0RExEWo9EVERFyESl9ERMRFqPRFRERchEpfRETERaj0RUREXIRKX0RExEWo9EVERFyESl9E\nRMRF2LX0ExIS6Nq1KytXrgTg9OnTDBw4kLCwMEaOHEl2djYAmzZt4pFHHqFPnz6sW7cOgJycHEaP\nHk1oaCgDBgzgxIkTABw5coT+/fvTv39/Jk+ebM/4IiIitxS7lX5mZiavvvoq7dq1syx74403CAsL\nIyYmhlq1arF+/XoyMzNZuHAhy5cvJzo6mhUrVpCWlsann36Kr68vq1evZujQocyZMweA6dOnExER\nwZo1a0hPT2f37t32+hFERERuKXYrfS8vL959910CAgIsy2JjY7nvvvsA6Ny5M/v27eOHH36gSZMm\n+Pj44O3tTYsWLYiPj2ffvn0EBwcDEBQURHx8PNnZ2Zw6dYqmTZsW2oeIiIhY52G3HXt44OFRePdZ\nWVl4eXkBUKlSJZKSkkhOTsbPz8+yjZ+fX5Hlbm5umEwmkpOT8fX1tWxbsI9rqVixDB4e7rb6sSz8\n/X1svk9bMno+MG7GhP//X6Pmu5Iy/nNGzldS/l80ej4wfkZH5bNb6VtjNpv/8fLitr1SamrmjQW7\nTklJF+yyX1sxej5/fx/DZzR6vpLwGho9o9HzFTByRr2GtmHrfMW9iXDo2ftlypTh4sWLAJw5c4aA\ngAACAgJITk62bHP27FnL8oJRfE5ODmazGX9/f9LS0izbFuxDRERErHNo6QcFBbF161YAtm3bRocO\nHQgMDOTgwYP89ddfZGRkEB8fT6tWrWjfvj1btmwBYOfOnbRp0wZPT0/q1KnDgQMHCu1DRERErLPb\n4f1Dhw4RFRXFqVOn8PDwYOvWrcyePZvw8HDWrl1L1apV6dmzJ56enowePZrBgwdjMpkYNmwYPj4+\ndOvWjb179xIaGoqXlxeRkZEAREREMGnSJPLz8wkMDCQoKMheP4KIiCEkPPW4syMUKwGov2S5s2PI\ndbJb6Tdu3Jjo6Ogiy5ctW1ZkWUhICCEhIYWWubu7M2PGjCLb1qtXj5iYGNsFFRERcRFOO5FPRESu\nrf6S5YY+Uc7IRyDk6jQNr4iIiItQ6YuIiLgIlb6IiIiLUOmLiIi4CJW+iIiIi1Dpi4iIuAiVvoiI\niItQ6YuIiLgITc4jIiL/yLAd45wdQa6TRvoiIiIuQiN9ERH5RxZ2mensCMV6MnLH5S+6ODeHUaj0\nb1El4XCbkX9RiIjcinR4X0RExEVopH+Lyfrm8i2Kl4Yb91hWSTgKISJyK9JIX0RExEWo9EVERFyE\nSl9ERMRF6DN9cbiRMWcBSIh53LlBRERcjEb6IiIiLkIjfXGa+kuWOztCsfz9fUhKuuDsGCIiNqWR\nvoiIiItQ6YuIiLgIlb6IiIiLUOmLiIi4CJW+iIiIi1Dpi4iIuAiVvoiIiIvQdfoiIgZl9DtSjnR2\nALlhKn1xGqP/QlvYZaazI4iI2JRKX0TE4Iz6BlT3zyh5VPriNEb9RWb0IxAiIjdLJ/KJiIi4CJW+\niIiIi9DhfRFxSfoYR1yRSl+kGCWhFIx6XoSIGJNKX0TsoiS8aQLjv3Hy9/chKemCs2PILUKlL/I3\nC7vMNPwv2oJCLSnFKiLGoNIXEbsy8kja6G/uRGxNpS9SApWEoxGgUhUxGl2yJyIi4iJU+iIiIi5C\npS8iIuIiVPoiIiIuQqUvIiLiIlT6IiIiLkKlLyIi4iJU+iIiIi5CpS8iIuIiNCOfiIj8IwlPPe7s\nCMUKt3zVxYkpjEOlf4t6MnKHsyMUK9z6JiIiYgcqfRERuSmR9QY5O4JV4b+87+wIhqLSv8UsDe9i\n+JucJDylf4Qit5Kl4cY9dK7fN4Wp9MVpjPwRBBj7F5m4BqP/G5GSR2fvi4iIuAiN9G8xCU89ToKz\nQ1wno46kC0ZXGmWJURj13wpg+I8TpTCN9EVERFyERvq3qPpLljs7wjUZeXRQEk6GBGO/hgWMntHo\n+aBkZJSSo0SW/muvvcYPP/yAyWQiIiKCpk2bOjuSiIiI4ZW40v/mm284fvw4a9eu5ddffyUiIoK1\na9c6O5aIiIjhlbjS37dvH127dgWgbt26nD9/nvT0dMqVK+fkZCIiYlRGnSp4ZMEXDjpXs8SVfnJy\nMnfddZflez8/P5KSkhxa+iNjzpIQ87jDnk9ERMQWSlzp/53ZbL7men9/H5s+3wf93oZ+Nt2ly7L1\n342tGT0fKKMtGD0fGD+jkfP5f7zB2REMpcRdshcQEEBycrLl+7Nnz+Lv7+/ERCIiIiVDiSv99u3b\ns3XrVgAOHz5MQECAPs8XERG5DiXu8H6LFi2466676N+/PyaTicmTJzs7koiISIlgMlv7UFxERERu\nCSXu8L6IiIjcHJW+iIiIiyhxn+k70rWm+927dy9z587F3d2djh07MmzYMMNlvHTpEpMmTSIxMZGN\nGzcaLt/+/fuZO3cubm5u1K5dm+nTp+Pm5vj3odfK+MEHH7B+/Xrc3Nxo2LAhkydPxmQyGSZfgTlz\n5vD9998THR3t0GwFrpWxS5cuVKlSBXd3dwBmz55N5cqVDZPv9OnTvPjii+Tk5NCoUSNeeeUVh2az\nlvHMmTOMGTPGst2JEycYPXo0Dz30kGEyAqxatYpNmzbh5uZG48aNGT9+vKHybd++nbfffhsvLy8e\nfPBBBgwY4PB8AAkJCTz33HM8/vjjRTI4pFfMclWxsbHmIUOGmM1ms/mXX34x9+3bt9D6Bx54wPzH\nH3+Y8/LyzKGhoebExETDZXzllVfMy5YtM/fq1cvh2cxm6/mCg4PNp0+fNpvNZvOIESPMu3btMlTG\nzMxM86BBg8zZ2dlms9lsHjhwoDkuLs4w+QokJiaa+/XrZx4wYIBDsxWwlrFz587m9PR0Z0Qzm83W\n8z3//PPmbdu2mc1ms3nKlCnmU6dOGS5jgZycHHP//v2d8npeK+OFCxfMnTt3Nufk5JjNZrP5iSee\nMH/33XeGyZeXl2fu2LGjOSUlxZyXl2d+8sknLb97HCkjI8M8YMAA84QJE8zR0dFF1juiV3R4vxjF\nTfcLl99ply9fnttvvx03Nzfuvfde9u3bZ6iMAKNGjbKsdwZr+TZu3EiVKlWAyzMrpqamGipj6dKl\nWbFiBZ6enmRlZZGenu7wOSGsvYYAkZGRjBo1yqG5rnQ9GZ3pWvny8/OJi4ujS5fLc6BOnjyZqlWr\nGirjlT788EP+/e9/U7ZsWUdHvGZGT09PPD09yczMJDc3l6ysLMqXL2+YfKmpqfj6+uLn54ebmxtt\n27Zl7969Ds0H4OXlxbvvvktAQECRdY7qFZV+MZKTk6lYsaLl+4LpfgGSkpLw8/O76jqjZAScPn/B\n9eY7e/YsX3/9Nffee6/hMgK88847BAcHExISQo0aNQyVb+PGjbRu3Zpq1ao5NNeVruc1nDx5MqGh\nocyePdvqLJqOzHfu3DnKli3LjBkzCA0NZc6cOQ7Ndj0Zr7Ru3Tp69+7tyGgW18pYqlQphg0bRteu\nXencuTOBgYHUrl3bMPn8/PzIyMjg2LFj5OTkEBsbW2iSN0fx8PDA29v7qusc1Ssq/evk6F9UN8Po\nGa+WLyUlhaFDhzJ58uRC/2Cd5WoZhwwZwvbt2/nqq6+Ii4tzQqr/c2W+tLQ0Nm7cyBNPPOHEREX9\n/TV8/vnnefnll4mOjiYxMdEyuZazXJnPbDZz5swZBg0axMqVK/nxxx/ZtWuX88JdkevvvvvuO+rU\nqeP0N/MFrsyYnp7O4sWL2bJlC//973/54YcfOHLkiBPTFc5nMpmIjIwkIiKC4cOHU716dScmcy6V\nfjGuNd3v39edOXPmqodrnJnRCKzlS09P5+mnn+aFF17gnnvucUbEa2ZMS0vj22+/BcDb25uOHTsS\nHx9vmHz79+/n3LlzPProowwfPpzDhw/z2muvOTSftYwAPXv2pFKlSnh4eNCxY0cSEhIMk69ixYpU\nrVqVmjVr4u7uTrt27UhMTHRoPmsZC+zatYt27do5OprFtTL++uuv1KhRAz8/P7y8vGjVqhWHDh0y\nTD6A1q1bExMTw+LFi/Hx8XHq0bGrcVSvqPSLca3pfqtXr056ejonT54kNzeXnTt30r59e0NlNAJr\n+SIjI3nsscfo2LGjsyJeM2Nubi7h4eFkZGQAcPDgQYcfsrxWvpCQEDZv3swHH3zAggULuOuuu4iI\niHBoPmsZL1y4wODBg8nOzgbg22+/5c477zRMPg8PD2rUqMGxY8cs6x39d2wtY4GDBw/SsGFDh2cr\ncK2M1apV49dff+XixYsAHDp0iDvuuMMw+QCeeuopUlJSyMzMZOfOnU59A3U1juoVzch3DbNnz+bA\ngQOW6X5//PFHfHx8CA4O5ttvv2X27NkA3H///QwePNhwGZ9//nn+/PNPEhMTady4MX379nX4ZT7F\n5bvnnnu4++67ad68uWXb7t2706+f429heK3XcOPGjaxatQoPDw8aNGjA1KlTHX7J3rXyFTh58qTl\nELozXCvjihUr+OijjyhVqhSNGjVi4sSJhnoNjx8/Tnh4OGazmfr16zNlyhSnXDpq7e/5oYceYtmy\nZdx2220Oz3Y9GdesWcPGjRtxd3enefPmjBs3zlD5tm3bxsKFCzGZTDz55JM8/PDDDs936NAhoqKi\nOHXqFB4eHlSuXJkuXbpQvXp1h/WKSl9ERMRF6PC+iIiIi1Dpi4iIuAiVvoiIiItQ6YuIiLgIlb6I\niIiLUOmL2MnJkydp0KABq1evLrT8wIEDNGjQgNjYWCcluzHHjx+3zE3/zjvvXHPGujNnztzwfOGh\noaE2fy1iY2MJDQ29occ0aNCA3NzcIstHjRrFmTNn2Lhxo+VudwXLAD7++ON/HljEQVT6InZ0xx13\nFLmt8caNG50yAYwtDBkyhE6dOhW7PjY2lv379zsukAPMmzevyK2AC5adOXOGNWvWOCmZyI3zcHYA\nkVtZQEAAly5dIjExkTvvvJOsrCzi4uIIDAy0bLN582ZWrlyJ2WzGz8+PadOmUbFiRWJiYvj444/x\n9PSkVKlSzJs3D19fX7p06cKgQYP48ssvOXnyJFOnTi0yu9jAgQNp1KgRiYmJJCUl8cwzz9C9e3fC\nw8Px8vLi6NGjzJ49m9TUVKKiosjNzSUnJ4dJkybRqFEj4uPjmTx5Mn5+ftx1112W/YaHh9OyZUv6\n9OnDunXrWL16NZ6enrRp04Y+ffowf/58zGYzFSpU4NFHH+WVV17h+PHjZGRk0L17d5588kmysrIY\nNWoUqamp1KpVi0uXLhV53WJjY5k/fz5Vq1bl1KlT+Pj4MG/ePNLS0nj22WepX78+d955J08//TSv\nvfYahw8fBqBt27a88MILAGRnZzNu3Dh+//13ypYty+uvv065cuV4/fXXLUcjqlSpwqxZs/D09ARg\n0aJF7N+/n4yMDKKioqhfvz5dunRh2bJlhfIVLBs/fjwJCQmW5xk1ahRt2rQBLs8AN3DgQKfcSEqk\nOBrpi9hZjx492LBhAwBbt26lY8eOlhnfTp8+zaJFi1i+fDmrV6+mdevWLF68GIBLly7x3nvvsXLl\nSqpVq8amTZss+yxVqhRLly7l2Wef5f3337/q8+bm5rJ06VIWLFjAa6+9Rn5+PgCZmZlER0dTuXJl\nxo4dy9SpU4mOjmbKlClMmDABgJkzZzJmzBhWrFhx1fs5nDp1ikWLFhETE8PatWs5e/YsOTk59OrV\ni4cffpgnnniC999/n4CAAKKjo1m3bh2fffYZR44cYdOmTXh7e7N27VrGjBlT7Fz3hw8fZty4caxZ\ns4YKFSpYjpj8+uuvDBs2jKFDh/L5559z8uRJVq9ezapVq/j666/55ptvAEhISODFF19kzZo1+Pn5\n8dFHH5Gbm0vp0qWJiYlhzZo1XLhwgT179lies27duqxcuZKwsDAWLFhg9e92xIgR1K9fn5kzZ9K/\nf38+/PBD4PJ9G44ePUqHDh2s7kPEkTTSF7GzBx54gF69ejFmzBg+/PBDxowZw6pVq4DLd05LSkqy\nTLeZnZ1tuQNYhQoVGDJkCG5ubpw6darIzUMAqlatyvnz56/6vAU3MapVqxYmk4mUlBQAy9THKSkp\nHD16lPHjx1sek56eTn5+Pj///DMtW7YELo+e/z6978GDB7nrrrsstwmNjIws8vyxsbH8+eeflpsW\nZWdn8/vvv5OQkGDZd0BAAHXq1Llq/nr16lkOq7do0YKffvqJLl26UL58ectjfvjhB9q1a4fJZMLd\n3Z1WrVpx8OBBGjduTJ06dahSpYrlZ/7555/x8PDAzc2NsLAwPDw8+O2330hNTbU8Z8Fc5y1atGDp\n0qVXzVWcBx54gPnz55ORkcEXX3zBQw895JTpfEWuRaUvYmd+fn40atSI9evXk5SURJMmTSzrvLy8\naNq0qWV0X+DPP/8kKiqKzz77jEqVKhEVFVVovYfH//3TLW4m7YKRfcE2BfPde3l5Wf7r6elZ7Hz9\nBYWVl5dXZJ3JZLJ6K2cvLy+GDRtGSEhIoeX79+8vVIZX5rzS32+BW5C/4FB8QY6/P6Zg2ZXPUbA8\nLi6ODRs2sGHDBsqUKcPzzz9f6PEFj7lyP9erVKlSBAcH88UXX7B161YmT558Q48XcQS9DRVxgB49\nejBv3jwefPDBQsubNGnC//73P5KSkgD4/PPP2b59OykpKVSsWJFKlSqRlpbGnj17LHequ14FJ9Qd\nPXoUNzc3/Pz8Cq338fGhevXq7N6927JdwSHtunXr8v333wOwd+/eIvsuyJ2eng7AyJEjOXToECaT\nyXIGfMuWLfn888+By8U+Y8YM0tLSqFu3Lt999x1w+eONo0ePXjX/b7/9xtmzZwGIi4ujQYMGRbZp\n1qwZe/fuxWw2k5ubyzfffGM5X+K3336znGEfHx9P/fr1SUlJoVq1apQpU4ZTp07x/fffF3pdCz7r\nL9jeGjc3t0Jn/Pfr14/Vq1djNpupUaOG1ceLOJpG+iIO0KVLFyZNmlTkzl6VK1dm/PjxPPPMM5Qu\nXRpvb2+ioqLw8/OjVq1a9O7dm5o1a/L8888zZcqUGzopLDc3l2effZaTJ08yceLEqx5qjoqKYtq0\nabzzzjuWWwkDjB07lldffZXbb7+dRo0aFXlc1apVGT58OI8//jgeHh60aNGCxo0bk56ezqhRo/D0\n9OTZZ58lMTGRfv36kZeXR6dOnahQoQI9evRgx44dhIWFUb169UJHPq5Ur1495s6dy/Hjxylfvjw9\ne/bk3LlzhbYJCQkhPj6e0NBQ8vPz6dq1Ky1btiQ2NpZGjRoxf/58jh8/Trly5ejRowcAS5cuJTQ0\nlDvvvJMRI0awcOFC2rRpg7u7O4mJiaxZs4bU1FRmzZpl9TWuV68eKSkpPPHEEyxbtox69eqRl5fH\nf/7zH6uPFXEG3WVP5BY0cOBAnn32WYKCgpwd5aYUnL3/9zkOjO7kyZMMGTLEctWFiNFopC8iYgOL\nFi1i8+bNvPrqqyp8MSyN9EVERFyETuQTERFxESp9ERERF6HSFxERcREqfREREReh0hcREXERKn0R\nEREX8f8APJFakkE7UX4AAAAASUVORK5CYII=\n","text/plain":["<matplotlib.figure.Figure at 0x7f411a6122e8>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"6erxQcXtl3ZH","colab_type":"text"},"cell_type":"markdown","source":["## 5 校准可靠性曲线"]},{"metadata":{"id":"bQmv-C27f_86","colab_type":"code","colab":{}},"cell_type":"code","source":["def plot_calib(models,name,Xtrain,Xtest,Ytrain,Ytest,n_bins=10):\n","    \n","    import matplotlib.pyplot as plt\n","    from sklearn.metrics import brier_score_loss\n","    from sklearn.calibration import calibration_curve\n","    \n","    fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(20,6))\n","    ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n","\n","    for clf, name_ in zip(models,name):\n","        clf.fit(Xtrain,Ytrain)\n","        y_pred = clf.predict(Xtest)\n","        #hasattr(obj,name)：查看一个类obj中是否存在名字为name的接口，存在则返回True\n","        if hasattr(clf, \"predict_proba\"):\n","            prob_pos = clf.predict_proba(Xtest)[:,1]\n","        else:  # use decision function\n","            prob_pos = clf.decision_function(Xtest)\n","            prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())\n","        #返回布里尔分数\n","        clf_score = brier_score_loss(Ytest, prob_pos, pos_label=y.max())\n","        trueproba, predproba = calibration_curve(Ytest, prob_pos,n_bins=n_bins)\n","        ax1.plot(predproba, trueproba,\"s-\",label=\"%s (%1.3f)\" % (name_, clf_score))\n","        ax2.hist(prob_pos, range=(0, 1), bins=n_bins, label=name_,histtype=\"step\",lw=2)\n","    \n","    ax2.set_ylabel(\"Distribution of probability\")\n","    ax2.set_xlabel(\"Mean predicted probability\")\n","    ax2.set_xlim([-0.05, 1.05])\n","    ax2.legend(loc=9)\n","    ax2.set_title(\"Distribution of probablity\")\n","    ax1.set_ylabel(\"True probability for class 1\")\n","    ax1.set_xlabel(\"Mean predcited probability\")\n","    ax1.set_ylim([-0.05, 1.05])\n","    ax1.legend()\n","    ax1.set_title('Calibration plots(reliability curve)')\n","    plt.show()"],"execution_count":0,"outputs":[]},{"metadata":{"id":"1BdDOaH4f_87","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.calibration import CalibratedClassifierCV"],"execution_count":0,"outputs":[]},{"metadata":{"id":"K5Prfp9kf_88","colab_type":"code","colab":{}},"cell_type":"code","source":["name = [\"GaussianBayes\",\"Logistic\",\"Bayes+isotonic\",\"Bayes+sigmoid\"]\n","\n","gnb = GaussianNB()\n","\n","models = [gnb\n","          ,LR(C=1., solver='lbfgs',max_iter=3000,multi_class=\"auto\")\n","        #定义两种校准方式\n","          ,CalibratedClassifierCV(gnb, cv=2, method='isotonic')\n","          ,CalibratedClassifierCV(gnb, cv=2, method='sigmoid')]"],"execution_count":0,"outputs":[]},{"metadata":{"id":"6JAEuhJmf_88","colab_type":"code","outputId":"424a7a87-db73-42ea-ec6c-8e86130b92cb","executionInfo":{"status":"ok","timestamp":1547540358226,"user_tz":-480,"elapsed":2565,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":423}},"cell_type":"code","source":["plot_calib(models,name,Xtrain,Xtest,Ytrain,Ytest)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABI0AAAGCCAYAAABtpjNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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mRnnS6H/9+ONqJk58kZCQ1mzfvpWcnOzycxs2/E7DhgFMnjyVn376oXwkj9lspmnTIEaNeoJH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\nAAAASUVORK5CYII=\n","text/plain":["<matplotlib.figure.Figure at 0x7f411cfc0940>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"CqiAJ4SJf_8-","colab_type":"code","outputId":"4caf467b-d3b6-4691-9998-14be6b4e1e4d","executionInfo":{"status":"ok","timestamp":1547540368390,"user_tz":-480,"elapsed":927,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["gnb = GaussianNB().fit(Xtrain,Ytrain)\n","gnb.score(Xtest,Ytest)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8650606060606061"]},"metadata":{"tags":[]},"execution_count":93}]},{"metadata":{"id":"UIwOEejlf_8_","colab_type":"code","outputId":"d6f2f60b-0316-4e0f-e2a7-35dac3e5aa86","executionInfo":{"status":"ok","timestamp":1547540371203,"user_tz":-480,"elapsed":876,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["brier_score_loss(Ytest,gnb.predict_proba(Xtest)[:,1],pos_label = 1)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.11760826355000836"]},"metadata":{"tags":[]},"execution_count":94}]},{"metadata":{"id":"ZemO-RUmf_9A","colab_type":"code","outputId":"250061cc-3828-4230-c63b-704ba7256287","executionInfo":{"status":"ok","timestamp":1547540418327,"user_tz":-480,"elapsed":908,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["gnbsigmoid = CalibratedClassifierCV(gnb, cv=2, method='sigmoid').fit(Xtrain,Ytrain)\n","gnbsigmoid.score(Xtest,Ytest)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8653030303030304"]},"metadata":{"tags":[]},"execution_count":97}]},{"metadata":{"id":"Y3uo62HXmHUK","colab_type":"code","outputId":"c8fa0d9b-4694-4771-d0d8-9e76449f6edc","executionInfo":{"status":"ok","timestamp":1547540421641,"user_tz":-480,"elapsed":1132,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["gnbisotonic = CalibratedClassifierCV(gnb, cv=2, method='isotonic').fit(Xtrain,Ytrain)\n","gnbisotonic.score(Xtest,Ytest)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8626767676767677"]},"metadata":{"tags":[]},"execution_count":98}]},{"metadata":{"id":"pMNiOA34f_9C","colab_type":"code","outputId":"9eb6d43c-8fb0-4b91-abd9-cd9b2ce152bf","executionInfo":{"status":"ok","timestamp":1547540380133,"user_tz":-480,"elapsed":1112,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["brier_score_loss(Ytest,gnbisotonic.predict_proba(Xtest)[:,1],pos_label = 1)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.09833190251353853"]},"metadata":{"tags":[]},"execution_count":96}]},{"metadata":{"id":"AcEFKlJFf_9E","colab_type":"code","colab":{}},"cell_type":"code","source":["name_svc = [\"SVC\",\"Logistic\",\"SVC+isotonic\",\"SVC+sigmoid\"]\n","\n","svc = SVC(kernel = \"linear\",gamma=1)\n","\n","models_svc = [svc\n","              ,LR(C=1., solver='lbfgs',max_iter=3000,multi_class=\"auto\")\n","              #依然定义两种校准方式\n","              ,CalibratedClassifierCV(svc, cv=2, method='isotonic')\n","              ,CalibratedClassifierCV(svc, cv=2, method='sigmoid')]"],"execution_count":0,"outputs":[]},{"metadata":{"id":"6Qay1B08f_9G","colab_type":"code","outputId":"88a229f6-0043-488a-df13-f32ba4b0128c","executionInfo":{"status":"ok","timestamp":1547540457542,"user_tz":-480,"elapsed":5764,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":423}},"cell_type":"code","source":["plot_calib(models_svc,name_svc,Xtrain,Xtest,Ytrain,Ytest)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABI0AAAGCCAYAAABtpjNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3XdYVMfXwPHvwlKlCEgR7L1g7xUL\nBGxRI8aSaBI1GhWjsWtQscbeS6zRmNcWS4waRbFFsRK7Ro29AdJ7W3bfPwj7cwMiFpqez/P4wN07\nd+bcYYXZc+fOVWg0Gg1CCCGEEEIIIYQQQrxAL68DEEIIIYQQQgghhBD5jySNhBBCCCGEEEIIIUQG\nkjQSQgghhBBCCCGEEBlI0kgIIYQQQgghhBBCZCBJIyGEEEIIIYQQQgiRgSSNhBBCCCGEEEIIIUQG\nkjQS4h3QaDT89NNPtG/fHnd3d1xdXfHx8SEmJuaVx7Zq1YqAgACuXLlC3759ARg7dizLly9/67j+\n+OMPYmNjARg9ejRHjhx56zpfpVevXuzevTvLMsnJyfz222/Zqu/48eP07t0btVqd7Rh27tzJl19+\nCWTvvF8s/1/pxz958oQqVaoA8Msvv7Bw4UIALl++zM2bN7MdW14IDg7Gw8ODkJCQvA5FCCGEKBAq\nVqyIm5sb7u7uNG/enAEDBnDx4kXt/nnz5rF58+Ys6zhx4gTPnj3LdN+LY4n0seDrCA0N5fDhwwA6\nY8icNnLkSFxcXDhx4sQ7r/vFsdbreNnYsyCP3YTIT5R5HYAQ74O5c+dy7tw51q5di729PfHx8Uyf\nPp0BAwbwf//3fygUilfWUb16ddauXftO41q8eDG1a9fGzMyM2bNnv9O638aNGzf47bff6NSpU5bl\nYmNjmThxIps2bUJP781y3G973unHP3nyRPva559/rv1+x44d1KlTh0qVKr1VOznJ3t6er7/+Gh8f\nH5YtW5bX4QghhBAFwsaNG3FwcECj0XDgwAEGDRrE4sWLqVevHiNGjHjl8evXr2fgwIE4Ojpm2Pfi\nWOJNnD17llOnTtG6descGUO+zL59+/D19aVEiRK50t67UtDGbkLkJzLTSIi3FBkZycaNG5k5cyb2\n9vYAmJqaMnHiRPr164dGoyEhIYFhw4bh7u5Oq1atmDVrVoZ6zp49i5ubm3Y7ODiYzz//nJYtWzJ4\n8GDi4+OBtKtRS5cuxd3dnWfPnnHv3j169OhBmzZtcHNzY+/evQCMGzeO+/fv06tXLwICAnSuwpw9\ne5bOnTvj4eFB165duXr1KpA24+bbb79l/PjxuLu707ZtW/75558Mse7cuZOvv/6aUaNG4erqSvv2\n7Xnw4EGm5/TfdkJDQ/Hy8uLSpUv07NkTgAULFuDu7o67uzu9e/cmODgYgM2bN9OwYUOcnJyAtKt+\nK1euxN3dndTUVO7cucPnn3+Ou7s7HTp00J7Hi14878OHD9OhQwfc3d355JNP+Pvvv7XlUlNTtefT\nuXNn7t27l+H4dEuWLOH7779n8+bN7N69mzlz5rBu3Tpq1KhBaGiottysWbOYPn16hpj+/PNP2rVr\nh7u7OwMGDCAyMjLD1bUXt3fu3ImXlxdffPEFs2fPpmnTply7dk1bdv369Xz33XcAbN26FQ8PD1q1\nasXw4cNJTEwE4OOPP+batWtyZU0IIYR4TQqFgjZt2jB8+HDmzZsH6M4K/+WXX2jTpg0eHh54enry\nzz//sHDhQs6cOcOoUaP4448/WLJkCd7e3nh6erJ+/XrtWCLdmTNn6NSpEy4uLixYsADIODZM375+\n/TpTpkzB19eX7777TqdcUlISEydOxN3dnTZt2jBz5kxSU1OBtDHkli1b8PT0pGnTpsycOTPT8332\n7Bl9+/bF3d2d9u3ba2eH9+rVC7VaTd++fTl+/LjOMUuWLGHs2LEMGDCAli1b0r17d8LCwrTHLViw\ngDZt2nDhwgUiIyMZOnSodqy5atUqnbp++ukn2rRpQ6tWrfDz8wNArVYzefJk7Vh61KhRpKSkaI+5\nffs2np6euLi44O3trT3nF+N7m7GbEB8ySRoJ8ZYuX76Mg4MDZcuW1XndyMiIVq1aoaenx+bNm4mL\ni+PAgQPs2rWLnTt3vnIa8okTJ1i8eDF+fn5ERUXx66+/avcFBwfj6+uLo6Mjs2fPpmXLluzfv58Z\nM2bw/fffk5KSwg8//ACkXSWrW7eu9ti4uDiGDh2Kt7c3Bw4coF+/fowcOVJ7+9eff/5Jz5498fX1\npUGDBmzYsCHT+E6dOsVnn32Gn58frVu3Zs6cOTr7X9aOtbU1w4cPp2bNmmzatIl//vmHAwcOsHfv\nXnx9fXFzc+P06dMA2u0XaTQafH19USgUDB48mI4dO+Lr64uPjw+DBg1CpVJlGq9KpWLs2LFMnToV\nX1/fDMm7Cxcu0LNnT/z8/GjevLl2UJiVHj16UL16dUaNGkWfPn1o1KgRf/zxh3b/oUOHaNeunc4x\n8fHxjBo1igULFmiv1C1atOiVbfn7+zN58mRGjx6Nq6urzi13fn5+tGnThoCAABYtWsSGDRs4cuQI\nZmZm2roNDAxo0aIFBw4ceGVbQgghhMioVatWXL58WXtBBtJmRS9atIhff/2VAwcO0LdvX44dO8aw\nYcOwt7dnzpw5tG3bFki75X7VqlWZ3hJ//fp1duzYwc6dO9m8eXOWF3mqVq2qvWiWnmBKt2HDBoKC\ngti3bx+7du0iICBAe0ER4Pz582zdupUdO3bwyy+/EBQUlKH+CRMmUL9+fXx9fVm5ciXTpk3jyZMn\nbNy4EUgbW7q4uGQ47uDBg3h7e3P06FGKFy/OypUrtfuuXbvGvn37qF27NvPnz8fS0hJfX182bdrE\n5s2btePi1NRUUlNT2b9/P1OnTmXChAmkpKRw6NAh7bns37+f69ev64y5zp49y8aNGzlw4ADnz5/n\n6NGjmfbdm4zdhPjQSdJIiLcUGRmJjY1NlmX69OnD8uXLUSgUWFpaUr58eZ3bnTLTvHlzrK2t0dfX\nx83NjUuXLmn3tWjRQvv98uXLtfex16lTh6SkpCzXrrly5QoODg7UqVMHAHd3dyIiInj69CkAZcuW\nxdnZGYAqVaoQGBiYaT1ly5alZs2a2jpevM8/O+2ks7CwIDw8nD179hAVFUWvXr3o1KkTKpWKGzdu\nUK1aNZ3y6ed+7949wsLC8PT01J67tbV1hjjSKZVKTp06pY25bt26PH78WLu/ZMmS1KpVC4A2bdro\n9Hd2tW/fnn379gFw8+ZN1Gq1tr10Fy5cwMHBgQoVKgAwatQoxo0b98q6S5UqRalSpYC0vkxPGoWH\nh3Pz5k1cXFw4cuQIbdu21c5469GjBwcPHtTWUaNGjTc6LyGEEEKAmZkZarWauLg47WtGRkYoFAq2\nb99OaGgobdq04euvv870+Bo1amBtbZ3pvg4dOqCvr4+NjQ316tV76XjmVY4dO8ann36KUqnE2NiY\nDh064O/vn6Ede3t7bGxsMozzUlJSOHXqlHY2uJOTEw0aNODMmTOvbLtBgwYUL14cgI8++kjnHFxc\nXLRLDRw/flxbf+HChXFzc9OJsXPnzgA0adIElUrFo0ePcHd3Z8eOHRgYGGBkZES1atV0xnHu7u6Y\nmJhgYmKCi4tLtsc72Rm7CfGhkzWNhHhLVlZW2tupXubBgwfMnDmTe/fuoaenR1BQEJ988kmWx7w4\nqDA3Nyc6Olq7bWlpqf3+xIkTrFixgoiICBQKBRqNJstFo8PDw7GwsNB5zdzcXDuF2NzcXPu6vr5+\nhum9mcVgYWGhE1922klnb2/PkiVLWLduHVOnTqVevXpMnjwZQ0NDUlNTMwyuChcuDEB0dDSJiYm0\nadNGuy82NpbIyMiXnvvGjRvZtWsXycnJJCcn66w19WI7ZmZmREVFvbSel2nVqhUTJkzg8ePH+Pn5\n4eHhkaFMRESETr8YGhpmq+4X+7t+/foEBwfz7NkzTp06hYuLC0ZGRsTExHDo0CFOnjwJpM3KenHq\nto2NTYb+F0IIIUT2PHnyBAMDA52xkoGBAevXr+fHH39kyZIlVKxYkUmTJlGxYsUMx7/4t/y/shr3\nvY7w8HCddiwtLXX+9puZmWm/z2ycFxkZiUaj0TnH9At8r5I+Rks/5mVj1/+OES0sLHj+/Ll228rK\nSvt9el+Eh4czdepUbty4gUKhIDQ0lC+++EJb7r/9l92Hf2Rn7CbEh05mGgnxlmrWrElYWBjXr1/X\neT0lJYUFCxaQkJDAlClTKF++PPv37+fAgQPZWnjvxaRFdHR0pgONlJQUhg0bxsCBA/H19eX3339/\n5aLbNjY2OokVjUZDVFTUK2dL/deLdURFRWWI73XaadiwIatWrcLf35+iRYsyd+5cNBpNlu3b2dlR\nqFAhDhw4oP138uTJDLezpbtw4QKrV69mxYoV+Pr6Mm3aNJ39/+3vFwc+2WVqakrLli05cOAAvr6+\n2unoL7KysiIiIkK7nZCQQFBQEPr6+qjVau15ZzVY1NfXx9XVlaNHj2pvTYO0PuncubO2P3x9ffnz\nzz9f+zyEEEIIkZGvry/169fPcMGnSpUqLF68mNOnT9O0aVMmTZr02nW/OA5JH1f9N6mTnURSkSJF\ndMZfkZGRFClSJNtxWFlZoaenpxNPdmbVAzrjm8zGhtmNMbO+WLBgAUqlkj179nDgwIEMt8dldkx2\nZGfsJsSHTpJGQrwlCwsL+vXrx5gxY3j48CGQlgiYOHEiN27cwMTEhLCwMCpXroy+vj7+/v48fPhQ\nu7D1y/z5559ERUWRmprKoUOHtLd5vSghIYH4+Hjt7WQbNmzAwMBAW7dSqcwwwKhevTqhoaHaKcP7\n9u3DwcGBYsWKvdZ5379/nxs3bgBpg6j/xpdVO0qlktjYWDQaDSdPnmTy5Mmo1WpMTU2pVKkSCoWC\nwoULo6+v/9IrW05OTjg4OGjX6AkPD2f48OEv7dfw8HBsbGxwdHQkISGBXbt2ER8fr03S3L9/X7u4\ndGbn8zJKpZKYmBjtdvv27dm8eTOJiYnan8uL6tSpQ0hICFeuXAHSbi9ctmwZVlZW6Ovrc+vWLQDt\nopMvk36L2tWrV2nevDmQdrXs4MGD2j7z8/PTWVwyPDz8pdPihRBCCJG59KenbdiwQfvgiXS3bt3i\n22+/JTk5GUNDQ5ydnbUX8P47RsjKvn37UKvVhIWF8ddff1G3bl1sbW0JCQkhLCyM1NRU9uzZoy3/\nsrpbtGjB9u3bSU1NJT4+nt27d2e6/tDLKJVKmjZtytatWwF49OgRAQEBNG7c+JXH/vXXX9rb3bIa\nS7Vo0UJbf3h4OIcOHdJZeiH9PP39/TExMaFEiRKEhYVRoUIFDA0NuXnzJhcvXtQZ8x08eJCkpCTi\n4+M5ceKEznqemZ3j64zdhPjQye1pQrwDQ4YMwdLSkoEDB5Kamoqenh6tW7fGx8cHgIEDB/LDDz+w\nfPlyWrdujZeXF4sXL6Zy5covrbNly5YMGTKEJ0+e4OzsTJcuXTKUSU9YderUCRsbGwYOHIirqyvf\nfPMNe/fuxcPDg+7du+vMqjE1NWXhwoVMnTqV+Ph4rK2tmT9//itnKP1XrVq1WL9+PQEBAZiamrJi\nxQqd/Vm1U6dOHebOnUuzZs04ePAg+/btw93dHUNDQ6ytrZkxYwZKpZLKlStz9epV7Ro9L1IoFMyf\nPx8fHx8WLlyInp4eX331FaamppnG26xZMzZt2oSrqyv29vaMHz+ey5cv8+2339KyZUsaNGjAxo0b\nuXjxIubm5ixcuDBb/eDq6sqcOXN4/Pgx48aNo2nTpsTGxtKjR49My5uYmLBkyRJGjRoFpK2lNHPm\nTIyNjRkyZAj9+vXDzs6OXr16Zdluw4YNGTFiBM2bN9de8axatSrffPON9ukmNjY2TJ48WXvM5cuX\n5T59IYQQIpt69eqFvr4+sbGxlC1bllWrVmVYa7FChQoUK1aM9u3bY2BgQKFChZg4cSKQdoFn+PDh\nfPvtt69sq1q1anh6ehIeHs4XX3xBuXLlAOjSpQudOnXC0dGRjh07ap/82qRJE3766Se6dOnC6NGj\ndWJ+/Pgx7dq1Q6FQ4OHhoXMrf3ZMnjwZb29vdu7ciYGBAdOmTaNo0aKvPK5x48ZMnjyZv//+G0dH\nR52nw71o2LBh+Pj44OHhgZ6eHv3796d69eo8efIEU1NT1Go17du3JzExkenTp6NUKunTpw9jxoxh\n586d1K1blzFjxvD9999TvXp1bdvpT+Bt0aIFzZo149mzZ5m2/7pjNyE+dArNq+4BEUKI/9i5cye/\n//4769evz9F2Vq1axf3797VPgiso2rVrx6JFi7QDvvxApVLh5ubG8uXLs0xWCiGEEEK8riVLlhAU\nFFRgH1efH8duQuQXcnuaECLf6tGjBydPnsz0cbD51b59+7C1tc13g469e/dSsWJFSRgJIYQQQrwg\nv47dhMgv5PY0IUS+ZW5uzpQpUxg7dizr1q3TPqo1v/rqq6+IiIhg8eLFeR2KjufPn7Ny5cocnxkm\nhBBCCFGQ5NexmxD5idyeJoQQQgghhBBCCCEyyN+X7YUQQgghhBBCCCFEnpCkkRBCCCGEEEIIIYTI\noMCsaRQSEpNjdVtZmRIREZ9j9Qtd0t+5T/o8d0l/5y7p79yVk/1ta2ueI/WKtyNjsPeH9Hfukv7O\nfdLnuUv6O3fl1RhMZhoBSqV+XofwQZH+zn3S57lL+jt3SX/nLulv8S7J+yl3SX/nLunv3Cd9nruk\nv3NXXvW3JI2EEEIIIYQQQgghRAaSNBJCCCGEEEIIIYQQGUjSSAghhBBCCCGEEEJkIEkjIYQQQggh\nhBBCCJGBJI2EEEIIIYQQQgghRAaSNBJCCCGEEEIIIYQQGUjSSAghhBBCCCGEEEJkoMzrAAqywMBn\n9O7dnYoVKwGQnJzMZ599gYtLy2wdf+FCALNnT6d//8G0auWa7XaPHvWjZUtX/vhjD/fu3cXLa9gb\nxZ8ZT88O/PzzVnbs2EatWrV59OjhG7eRHmd2+Puf4Nixw3z/vc9rtyOEEEIIIUR+1GfmkRypd93Y\nVq8ss2PHNnx9/8DQ0JCkpETatevIrl2/smHDFm0ZjUaDp2cH1qz5GWNjExYvns+tWzcwNDTCwsKC\nESPGYm/vkCPnIIQoGCRp9JZKlCjJ0qWrAIiOjuKrrz6jYcNGGBkZv/LYy5cv8sknXV8rYZSSksLW\nrZuynYx5U716fQnAo0cP37iOX37ZkONxCiGEEEIIIXQFBj5jz57fWLPmZ5RKJY8fP2LWrGkolQY8\neHCfUqVKA3DlyiVKliyFlZU1s2ZNp2jRoowZ8z0AR4744eMznhUr1uXlqQgh8liOJo1u377NoEGD\n+PLLL/n888919p06dYr58+ejr69P8+bNGTx4cE6GkissLCyxsSlCWFgYhoaG/PDDVFSqFPT09Bgz\nZgIODg50796ZChUq4excnX37fkepVGJjU4QiRWxZuXIZSqUSOzt7xozxxsDAgIUL53LjxjX09fUZ\nNWocu3bt4O7dO8ydO5MqVaoCsHz5YkqUKEH79p0A+PzzrixbthpLy8IAqFQqpk2bRHBwIIaGRnh7\nT8bU1JTJk71JSEggMTGR774bRZUqztpzmT7dhxYtWgMQGPiUkSO/5fnzYD79tCft23eke/fONGzY\nBCsrKxo3bsb8+bNQKpXo6ekxdepM9u7dzZ07txk/fhQzZsxh5cplXLlyCbU6lS+//IIGDVy4e/cO\n06ZNxMLCEkfHYrn80xJCiIIhq6vU2bnSLIQQIu+9q9/X2Z25FBsbS3JyEikpKSiVSooXL8HSpavY\nvPkXDh8+SN++AwA4cuQQbm4exMfHce7cabZt262to1UrV+rVa/BO4hZCFFw5tqZRfHw8U6dOpVGj\nRpnunzZtGkuWLGHz5s34+/tz586dt26zTh1n+vf/Uru9d+/v1KnjzG+/7dC+NmjQ19Sp40xycjIA\nYWFhlCpVijFjhmvLbNy4njp1/pdAya7AwGdER0dhZ2fP6tUr6N79MxYtWsGnn/Zgw4Y1ADx79pQv\nv+zHp5/2oE2b9nTt2p3WrT9i4cI5zJw5j8WLf8Ta2pqjR/04f/4sz58Hs2rVegYMGMzhw4fo2bMX\nJUqUZOTIsdp2PTzacvjwIQDu37+Ho6OTNmEEsH//XmxsbFixYh0dOnTi5Mk/CQsLo337TixZspJv\nvvHi//5vw0vP6/HjR8ycOZ8lS1aydu1KNBoNKpWKhg0b88UXfYmMDOe770axZMlKqlWrwcGD++nZ\nszdmZmbMmDGHy5cvEhwcxLJlq1m06EdWrFhBUlIi69evoU+f/ixatAJ9fVleSwghhBBCiHehfPkK\nVK5cla5dP2b6dB8OHz6ESqXC1fUjjh07DIBareb0aX9cXFry9OkTSpQoib6+vk495ubmeRG+ECIf\nybGZRoaGhqxevZrVq1dn2Pf48WMsLS0pWrQoAC4uLpw+fZpy5crlVDg55tGjh3h59QfSztnbezJK\npZJr167w6NFDNmxYi1qtpnBhKwCMjU0oU6asTh3h4WE8efKY8eNHAZCYmIilZWFCQp5TrVoNAGrW\nrE3NmrUJDHyWIYYyZcoRGxtDREQEJ08ex83NQ2f/rVs3qVu3HgCuru5A2tWHDRvWsHnzRlJSUjA2\nfvntdNWr10SpVGJpWZhChQoRFRUFoJ3pZGVlw4oVS0hKSiQ0NCRD+1evXub69avaflKr1YSGhvLg\nwT2cndPOr1atOpw5cyrLvhZCiA+BKlVNfJKKhH//5ScREeFYW5vmdRhCCCGyYcKEKTx4cJ9z506z\nadPP/PbbdhYv/pHCha24e/cO0dFRVKhQCVPTQoACtVqd1yELIfKhHEsaKZVKlMrMqw8JCcHa2lq7\nbW1tzePHj7Osz8rKFKVSP8sy/11/56uvPuOrrz7Tee3XX7fobNvamvPgwQOd14YPH8Lw4UOybAsg\nKakQZcqUZuvWzRn2GRsbsXz5Uuzs7HReNzQ0wNY2LWNfqJARZmbGODhYYW9vn6GedevWoVarteXT\n21Qq9bC1Ncfc3BhTU0Nsbc3p1KkjFy6c4sqVCwwcuAITExPtMWZmxpiZGenUs3XrBkqUKMbixQu5\nevUqs2fPxtbWHH19PYoUMcPY2ABLSxPU6kRMTAy1x6bv19fXw8HBikKFCjF8+AK+/vprmjdvztq1\na4mPj8fW1hyFQoGtrTlWVuZ06/YpAwYM0Dk/fX097X4zMyOMjQ10YhTvlvRt7pL+zl2v098dRux+\n6b498zq+VRypag0JiSnEJaqIT0whLuHffy9u//t9bEIK8QkpxCeqiHthX3JKarbby+33WZ8+PXF1\ndWXo0KG52q54Pw0+MjqvQ3gry1rNzusQhHgpjUZDcnIypUqVplSp0nTp0o3PPvMkODgINzcPjh71\nIyYmWnux18nJiYcPH5CcnIyhoaG2nps3b1CpUpW8Og0hRD5QYBbCjoiIz7G6bW3NCQmJee3jwsPj\nUKnUmR5boUIVfvttH507e/LXX+cJCwvjo4880Gg02vJxcUkYGCSSnKxHaqqac+cuU7p0GbZv30LN\nmnUoXrwsv/yyno4du3H79k327NnNZ599QVJSCiEhMcTEJBIfn0xISAyNGrVg7NgRFC9enNhYFbGx\n/4upZMlyHDt2grp1m+Lvf4K7d/8hLCyUsmXLExISw+7d+4iPTyQkJIbUVDWhobEkJqYQFZVATEwi\nAQF/ERQUSXR0NLGxcaSk6GvLxcerCQ0Nw8zMhqdPw/DzO0LVqtW0dYWExFCiRDmWLVtEp07dSUlJ\n4aefVvDNN8NwdCyOv/95GjRoxLFjJ1GpUt7o5yBe7U3f4+LNSH/nrnfZ34+eRGhn+CQkpRKflPLv\nrJ9U4hNTSEhKJSFJpZ0JpP2amPY1MTn7CZ90Sn0FJkZKTIyUONqYYmKkxNRIiYlx2teD519+USU3\n3mdJSUkYGRkBMGqUN1eunM+xdiXZKoQQ78bevbu5dOkC3t6TUSgUxMXFolarsbKyokWL1owaNZSk\npCQGD057QrKpaSGaNnVhzZoVDBqUdmHg2LHDbN++lSVLVqJQKPLydIQQeShPkkZ2dnaEhoZqt4OD\ngzPMyCno+vbtz4wZk/Hz80WhUDB+/KQsy48dO5EZMyZjYGBAkSK2fPzxJxgaGnLixHEGDeoHwIgR\nYylSpAgqVQre3mNo3Lip9nhraxtMTExxdfXIULerqzsBAefw8uqPvr4Sb28fQkNDmDZtEkeP+tGl\ny6f4+R1k377fM42tRIlSTJgwlqdPH9O//6AMfzS6dOnGuHEjcXJyokuXbixYMJtWrdyoUKEiX3/d\nm9Wrf6ZWrToMGPAVoKF3714AfPFFX2bMmMyvv27G0dEJlSrldbpYCJGP5OZizRqNBlWqhqSUVJJT\nUklUQ9DzaJKSU0lKUZOckkrSi/+SU0lOUWu3szJ4wZ+vFYtCQVqCx0iJXWETbfLH1Fj5v+91tvUx\nNTL492va6wavmEWbVdIop23e/AszZkxh//7DFCtWnMqVq9C8eQNJiop3qqDN2CnoM6RE3sjuAtbv\nStu2HXj48AH9+3+BiYkpKpWKYcNGYWRkjJGRMdbW1lhYWOrMKho6dATLly+md+9umJtbYGdnz4wZ\ncyRhJMQHTqHRaDQ52cCSJUuwsrLK8PS0du3asXLlShwcHOjWrRtz586ldOnSL60nJweo78OsgMjI\nSEaMGMLq1RvQ08vfi0q/D/1d0Eif564Psb+zGgzP+qaRNmGT/G9i58WkjjbJk6zW3U7RTfZo9yWr\nUefQn67qZW20SSCTDMkepTbhk77PyEA/xwfTefn0tC1b/o+JE8exatV6WrRIaysn398y0yh/yqmf\nd3rypaAmjQpa3PBh/n3KS7a25lneEv025OmZmZP3eO6S/s5deTUGy7GZRteuXWPWrFk8ffoUpVKJ\nr68vrVq1olixYri5ueHj48OIESMAaNu2bZYJI5G1P/88xtq1Kxky5Lt8nzASQnx4xvx4+o2P1VMo\nMDLUw9BAHyMDfSxMDTEy0MfI4N/XDPWxNDdGk6r+t4zev/vT9qUfZ/TCvtFZxDOsa403jjWn5OYH\ng+TkZDZuXE/v3l9hYGBAt27pXbYKAAAgAElEQVQ9cXPzwMbGJtdiEEKI94kkd4QQBV2OJY2cnZ3Z\nuHHjS/fXq1ePrVu35lTzH5TmzVvQvHmLvA5DCPGBUaWquXY/nNPXgrIs18TZAUPDF5M3LyR9DPT/\nl+x5oUz6PqW+4pUzeeQq17uzYMEc5s2bRVJSEoMGDUGhUEjCSAghhBDiA1ZgFsIWQgiR9zQaDQ+C\nYjh9LYizfwcTE//qtcj6tpenruRnLz4p55tvBpOQkMDnn/fO46iEEEIIIUR+IEkjIYQQrxQWlciZ\nG0GcuhZEYFja0yzNTAxoXacYjZ0dmLohII8jzD65VeB/zpw5jZdXfxYsWEqzZi5YWhbGx2daXocl\nhBBCCCHyCUkaCSGEyFRCkoqAW885fS2IW48i0QBKfT3qVrKjcVUHnMtYo9SXddQKihUzj2X6el/P\nJfz993WaNXPJ3YCEEEIIIUS+J0kjIYQQWqlqNdfvR3D6ehAXb4eQrFIDUKGYJY2cHahXyQ5TY4MM\nx8nsnYKtf/9BeR2CEEIIIYTIhyRp9JZ27NiGr+8fGBoakpSUSP/+g7G3t2fChLFs2LBFW06j0eDp\n2YE1a37G2NiExYvnc+vWDQwNjbCwsGDEiLHY2ztkqH/q1Al07twVW1s7pk6diFqtxsamCBMmTNGu\nQZHu3r07jB07gm7detKlSzcAVCoV06ZN4unTx5iaFmLq1FlYWFjw00+rOXPmFBqNhsaNm/Lll/2Y\nNGk83bt/RuXKVXO204QQ+YpGo+FRcCynrwdx5kYw0XHJANhbmdDI2YFGVR2wLWySx1GKnHS735c6\n2xXWrM+TOMT/JCQkMHbsWMLCwv5dmHwQlSpVYvTo0aSmpmJra8ucOXMwNDTk999/Z8OGDejp6fHp\np5/StWtXUlJSGDt2LM+ePUNfX58ffviB4sWLc/PmTXx8fACoWLEikydPztsTFeI9N/jI6Bypd1mr\n2VnuDwx8hrf3GNauffmDibKyaNE8unbtjqOjU4Z9cXGxXL9+jfr1G7Jx43pq1aqNs3P1N2pHCJH/\nfTBJoz4zj7x03555Hd+ozsDAZ+zZ8xtr1vyMUqnk8eNHzJo1jaVLV6FUGvDgwX1KlSoNwJUrlyhZ\nshRWVtbMmjWdokWLMmbM9wAcOeKHj894VqxYp1O/v/8JjIyMcXauzowZk/nkk09p1cqVlSuXsW/f\n73Tu7Kktm5CQwIIFc6hTp75OHb//vovCha3w8ZnO7t07uXLlImXLlufu3TusXPkTqampfPaZJ+3b\nd2TIkOGMGzecVas2vPJpRUKIgi88OpEzN4I5fS2Ip6FxABQyVtKythONnR0oU9RCfhe8B9RqNamp\n6rwOQ7ymo0eP4uzszNdff83Tp0/p06cPtWvXpmfPnrRp04b58+ezfft2OnXqxLJly9i+fTsGBgZ4\nenri5ubG0aNHsbCwYN68eZw8eZJ58+axcOFCpk+fzvjx46levTojRozg+PHjuLjIrYlCCF1Dh454\n6b5bt25y7twZ6tdvSK9eX+ZeUEKIPPHBJI1yQmxsLMnJSaSkpKBUKilevARLl64CwNXVncOHD9K3\n7wAAjhw5hJubB/HxcZw7d5pt23Zr62nVypV69RpkqH/bts14eQ0F4OLFvxg5chwATZo0Y/PmjTpJ\nIwMDA+bOXcQvv2zQqcPf/wR9+/YHoGPHT7SvT5s2C4CYmBgUCgWmpoUwNTWlePGSBAScyzQeIUTB\nl5Ck4sLtEE5dC+Lmw4h/1ylSUKeCLY2dHahW1kbWKXqPPHz4gCkT5lLXuT1gnNfhiNfQtm1b7feB\ngYHY29tz9uxZ7cygli1bsm7dOkqXLk21atUwNzcHoHbt2ly4cIHTp0/TqVMnABo3bsz48eNJTk7m\n6dOnVK9eXVvH6dOn8yxpNHTTcwBub/oyT9p/U0PTv5G7csVreNXMoOx6m5lLd+/eYf78Wdqxv7e3\nD6amhZgyZQJBQYFUq1adI0f82LXrD7y8+jN8+GhUKhXz5s3CwMAAQ0NDJk/+gfnzZxMfH0fx4iW4\ndu0KLVq0pkGDRkybNong4EAMDY3w9p6Mra3dOzlnIUTeem+SRtuO3OH8zedvdGzfaQdJTdVkeL1e\nJTs+bVXupceVL1+BypWr0rXrxzRq1ISGDZvg4tISpVKJq+tHDB/uRd++A1Cr1Zw+7c+AAYN5+vQJ\nJUqURF9fX6eu9MFeOpVKxb17dyhXrgKQNpMo/XY0KytrwsLCdMorlUqUyow/zqCgZ5w5c4rlyxdj\nY2PDiBFjsbCwBGDhwrkcPnwQL69hmJqaAlCjRi0uXAiQpJEQ7xG1WsONh+GcuhbEhdshJKekzTop\n52RJY2cH6layw8wk4zpFomB78iCCM4eDqVvFE02qGpk0VjB1796doKAgfvzxR7766ivtWMDGxoaQ\nkBBCQ0OxtrbWlre2ts7wup6eHgqFgtDQUCwsLLRl0+sQQnwYFi2ay6BBQ6la1ZlNmzby669bqFix\nMsnJSaxatR5//xNs27ZZ55g//thD586eeHi046+/zhMeHkbPnr24d+8uHTt+wrVrVwDYv38vNjY2\n+PhMx8/Pl5Mn/9S5wC2EKLjem6RRXpkwYQoPHtzn3LnTbNr0M7/9tp3Fi3/E1taOwoWtuHv3DtHR\nUVSoUAlT00KAArX61bcJREVFYmlpmemtIRpNxgTXy2g0GkqUKEmfPv1Zv34NGzeuZ/DgtGtkw4aN\npE+f/gwZMoBq1Wrg6OiEnZ0dV65cynb9Qoj86/HzWE5dC+TMjWCiYtPWKbIrnL5OkT12VqZ5HKHI\nCcePnOHp3RSiwlIBcCxphmUtNX//Fp/HkYk3sWXLFv7++29GjRql8/f/ZWOB13k9u+MJKytTlEr9\nVxd8Tbf//dpk9453XndO8u/YBQBbW/NXlMyfCmrcBd277vdX1ZeUVAilUk+n3KNHD2jRohEArVs3\nZ+nSpdjYWNKwYX1sbc35+GMPvL1HY2trjqGhEiurQrRv3wYfHx/CwoJo27YtFSpU4NGjfzA1NcTW\n1hxjYwMsLU24dOkuTZo0wtbWnB498jZZJO/x3CX9nbvyor/fm6TRp63KZTkrKKs1jdZ6f0RISMxr\nt6nRaEhOTqZUqdKUKlWaLl268dlnngQHB+HgUBQ3Nw+OHvUjJiYaNzcPAJycnHj48AHJyck6C1nf\nvHmDSpWq/KeF/yWMTExMSUpKxMjImJCQ5xQpUiRbMVpb21CzZh0AGjRoxNq1KwkODiIiIpxKlapg\nYWFBtWo1+PvvG5kudCeEKFgiYpI4eyOYU9eCeBISC6StU9SilhONqzpQ1knWKXpfRYTGccz3BkGP\nEwEoVqowJeoW4mCkL88ePqCOaRI1bsRipNIQY6qHebysc5SfXbt2DRsbG4oWLUrlypVJTU2lUKFC\nJCYmYmxsTHBwMHZ2dtjZ2REaGqo97vnz59SsWRM7OztCQkKoVKkSKSkpaDQabG1tiYyM1JZNr+NV\nIiJyNuH4JmOw/KAgxm1ra14g4y6oXvxw9677/VX1hYfHoVKpdcqp1RrtdkhIFCqVmtjYRPT09AkJ\nidEmkkNCYkhOVhEREUe5cs78+ON6Tp06wYgRo/DyGkZMTCLx8cmEhMSQmJhCVFQCycmpREbG5/n7\nS97juUv6O3flZH9nlYyShSvewt69u5k9e7r2F2xcXCxqtRorKysAWrRozfnzZ7l8+RKNGjUBwNS0\nEE2burBmzQptPceOHWbp0oU6V/wsLQsTHR2lfa1u3focO5aW+Dp+/AgNGjTOVowNGjTm7NlTANy6\n9TclSpQkMjKSuXNnolKpSE1N/ff1EgCEhIRgZ2f/Nt0ihMhlScmpnL4WxLytlxi53J9tR+8QGBZH\nrfJFGNzZmfleTentXpFyxTKfvSgKtpioRA7v/Zuta88T9DgOtSKWYtVSiax+m7X3VmF19ib99kRR\n/0oMqfpwvLYZGzrY5HXY4hUCAgJYty7tARmhoaHEx8fTuHFjfH19ATh48CDNmjWjRo0aXL16lejo\naOLi4rhw4QJ169alSZMmHDhwAEhbVLtBgwYYGBhQpkwZAgICdOoQQnwYSpcuq72d7OLFC1SsWBkn\np2LcunUDgHPnzpCamqpzzI4dW4mOjuKjj9rQrVtPbt++iUKhyFCuUqUqXLhwHkhbU/Xnn3Uf8COE\nKLjem5lGeaFt2w48fPiA/v2/wMTEFJVKxbBhozAySlts1MLCAmtraywsLHVmFQ0dOoLlyxfTu3c3\nzM0tsLOzZ8aMOTof5pRKJaVLl+Hu3TuUK1eevn0HMG3aRHbv3omDQ1HatGkPwKRJ4xg/fhL3799n\n6dIFBAUFolQqOXr0MDNmzKFr1+5MmzaJvXt3Y2Jiire3D9bWNri4tGTgwL6AhkaNmlK+fEUALl++\ngIdHu9zrRCFElrKaJTmie01OXwvir1shJKWkDd7KOlrQyNmB+pXtZZ2i91xCfDJnjt/hxqVA9BT6\nWBUxpW6zktwz+ptD9/0ocyKKvtcTMIlXoWdiglWnDpRz/Yiaxv8uiO2Wt/GLrHXv3p3vv/+enj17\nkpiYyMSJE3F2dmbMmDFs3boVR0dHOnXqhIGBASNGjKBv374oFAoGDx6Mubk5bdu25dSpU/To0QND\nQ0NmzpwJwPjx45k4cSJqtZoaNWrQuHH2LkLlhMPlvkz7OvNYnsXwRv6Nu0LeRiEKmLdZwPpNPXr0\nEC+v/trtfv2+YeXKZSgUCszNzRk/fhJKpQH79v3OwIF9qVWrjnbt03ROTsWZMGEsZmZmGBgYMH78\nJCIjI/jxxyU6C127uroTEHAOL6/+6Osr8fb2ya3TFELkMIXmdRbIyUM5Oe0tv06rO3nyOGfOnNI+\nNS2nhYeHMXr0d6xevSFHZyPk1/5+n0mf56532d9ZJY3SFbE0plFVBxo7O2Bv/eGtU/Shvb+Tk1Rc\nOveYK+efkJKcSlxCBH/fP8rA6V/xx6ODFL75lEbX4rGMUaEwNKRwazes3dugb2b2TtrPq6nRIu/k\n1M97RUFLFv3HwLEt8jqE1/ah/b7Ma7a25ny6dWCO1P2unsYWHR3FhQsBtGjRmpCQ5wwdOpBNmwrW\nOmMvkvd47pL+zl15NQaTmUb5WNOmLhw96se1a1dxdq6W4+0tXjyf774bLbevCFFANK/hSGNnB8rL\nbWcfBJUqlesXnhHgf5/kJDUmpgY0cCkN1lEYR4Ry7Oh6XC/HUSRKBfr6FG7VGuu2HVAWLpzXoQuR\npYKWfCnoyS6Ru95VcienmJoW4sgRPzZt2ohGo2bIkOF5HZIQIp+RpFE+N2HC1Fxry8dneq61JYR4\ne1+2qZTXIYhcoFaruXk1iICTD4mLSSI5OYGzl/cwfcEYLiZd4p8zh2l4KYaiYSpQKLBo3BSbjzti\nUMQ2r0MXQgiRzymVSqZM+SGvwxBC5GOSNBJCCCHyIY1Gw71bIZz78z6R4QnoK/Wo2aA4D4PPU69a\nRXYGrKTGXyF0Dk4BwKxOXWw6foKRo2MeRy6EEEIIId4XkjQSQggh8hGNRsOTBxGcPX6PkKBYNGh4\n+OwC4yd/Q7heKJdPP6LSmTuUfZIMgElVZ2w7e2JcqlTeBi6EEEIIId47kjQSQoh86u7TqLwOQeSy\noKdRnD1+n2ePIgEoV9mOA8d+4viFg1QIMMD27HVaPkhCASjLlMbBszumFSrmbdBCCCGEEOK9JUkj\nIYTIh9QaDZv8bgMw7vPalC8mixm/z8JCYjn3530e/BMGgLFZCu09G1LYzoQUpwY0dYij0rYL6GsA\nRwccPXtQqFp1WQBdCCGEEELkKEkavYXAwGd4e49h7dqNb1zHokXz6Nq1O46OThn2xcXFcv36NerX\nb8jGjeupVas2zs7VX1nnP//cZv361UyfPodNm37m6FE/QEGfPl/TqFHTDGXnzZuJQgFly5Zn5Mhx\nAKxdu5IzZ06hVOrzzTffUqNGTcLCQpk+fTJJSYlYWVkxfrwPjx8/4pdf1jN16sw37gMhREb+VwO5\nHxhDwyr2kjB6j0VHJnD+xANuXw8GwKGYBQePr2fH7vUUrrIKxZZzVPg7HGUqqGwsse/SA4u69VHo\n6eVx5EIIIbLjdr8vc6TeCmvWv7LMjh3b8PX9A0NDQ5KSEmnXriO7dv3Khg1btGU0Gg2enh1Ys+Zn\njI1NWLx4Prdu3cDQ0AgLCwtGjBiLvb3DS9sYO3Y4M2fOf63Yjx71o2VL19c65syZUwQGPqNzZ8/X\nOk4I8fY+mKTR4COjX7pvW7cVuRiJrqFDR7x0361bNzl37gz16zekV68vs13n3Lk/MHnyDzx79hQ/\nv4OsXPkTsbGxDB7cj/r1G6Gvr68tu3jxPIYOHUHlylXx8fme06f9sbGx4fz5s9rjxowZxooV69i4\ncT3NmrnQubMnBw7sY/v2LfTu3QcbmyJv9MtfCJG5+EQVO47dxdBAD88WZfM6HJED4uOS+cv/ITcu\nPUOt1mBtW4iGLmUoUdYay1LBNC2bSrltvhipNCSbm2DVyZMiTVugeOH3txBCCPEygYHP2LPnN9as\n+RmlUsnjx4+YNWsaSqUBDx7cp1Sp0gBcuXKJkiVLYWVlzaxZ0ylatChjxnwPwJEjfvj4jGfFinUv\nbed1E0YpKSls3brptT83NGzY+LXKCyHenQ8maZSb7t69w/z5s1AoFJiaFsLb2wdT00JMmTKBoKBA\nqlWrzpEjfuza9QdeXv0ZPnw0KpWKefNmYWBggKGhIZMn/8D8+bOJj4+jePESXLt2hRYtWtOgQSOm\nTZtEcHAghoZGeHtPxtbWTtv25cuXsLa2xsHBgb17d9OwYWMMDAywsrLCwaEoDx7cp2zZckDaL+3A\nwGdUrlwVgCZNmhEQcI4qVZypWLESenp6WFhYUKiQGYGBz3jy5BEeHu0AaNCgERMmjKV37z54enZj\n+nQfSRoJ8Y7sOXWf6PgUOjcvg7WFcV6HI96hpMQULp17zJXzT1ClqLEobIyyUDhLVo6m4UdbOP5/\nP2Fz+jrFkjQkmRhg9HEbyrl1QM/AIK9DF0II8RayMzMoO7I7cyk2Npbk5CRSUlJQKpUUL16CpUtX\nsXnzLxw+fJC+fQcAcOTIIdzcPIiPj+PcudNs27ZbW0erVq7Uq9dAp14vr/4sXbpKu92uXWv27TvM\n/v172blzG0qlAeXKVWDEiDGZfiZavfpH7t69w9y5Mxk2bCSzZ0/n2bOnJCcn06/fN9Sv35Bu3TrR\nseMn+PufIDk5mUWLlnPs2BHu3buLl9cw/u//NnDs2GEUCj2++caL2rXrvn3Hig9GTs3+yw23eXe/\nS17He5M02nlnLxefX32jYwfv+Z5UtSbD67XsqvFJufavXd+iRXMZNGgoVas6s2nTRn79dQsVK1Ym\nOTmJVavW4+9/gm3bNusc88cfe+jc2RMPj3b89dd5wsPD6NmzF/fu3aVjx0+4du0KAPv378XGxgYf\nn+n4+fly8uSfOtM0L1w4T40atQAIDw+jcGEr7T4rKyvCwkK1SaOoqEjMzc1f2G9NWFgoZcqU5eef\n15KYmEh8fBz//HOb8PBwypQpx+nTJ6lUqTJnzpwiMjICgGLFihMcHERiYiLGxvIBV4i3ERgWh1/A\nE4pYGuNRv3hehyPekZSUVK799ZSLZx6RlKjCtJAhjVqWpHKNoqxdvYwWZYsQNnsajglqkg31SPmo\nKVU69ETfxCSvQxdCCFEAlS9fgcqVq9K168c0atSEhg2b4OLSElfXjxg+3Iu+fQegVqs5fdqfAQMG\n8/TpE0qUKKlzRwKg81khK1u2/MLs2Quxt3dg377fSUpKzPQzUc+evbhx4xojR45l//69GBoasnTp\nKkJDQ/DyGsCWLTtJTU2lRIlS9OzZm0mTxhEQcF7bzuPHjzh27DArV67n2bOn/PLLekkaCZHD3puk\nUX7y4MF9qlZ1BqB27br89NMqjI2NqVatBgCNGjXJ8Au5aVMX5s6dyePHj2jd2o2SJUtx/XrGJNit\nWzepW7ceAK6u7hn2h4aGULt2vUzj0mTMi/1nf1qB0qXL8PHHnRk2bBCOjk6UK1cBjUZDr15fMXfu\nD3h59adRoyba8gA2NjaEhYXi5FQs60aEEFnacvgOqWoN3VqVx0AptyIVdKmpam5eCeIv/wfExSZj\naKSkgUtpHgYGULl6fe4d20PdW1dpYWBHSrKGyMbOVPfsh7GFrGMlhBDi7UyYMIUHD+5z7txpNm36\nmd9+287ixT9SuLAVd+/eITo6igoVKmFqWghQoFarM60nPj6e0aOHAXDnzm28vPrj6OjE+PGTtGVc\nXd0ZP34U7u5tcHV1x8jIONPPRG3bdtAec+vW39SqVQeAIkVsMTQ0IDo67cmx6RfBbW3tiYuL1R5z\n+/YtqlRxRk9Pj2LFijN27IR312Hig5IXM3beRl7OkHpvkkaflGuf5aygrNY0WtZhOiEhMTkRFipV\nCnp6emg0GvT00j4AKhSKDE+8qVu3PmvW/MypUyeYNs0HL69hmdanr6+HOpNZUS9Kr7tIEVsePXqo\nfT0k5DlFihTRbhcubEVU1P8e6R0aGkKRIrYAdOnSjS5dugEwYMBXFC1aFHNzcyZPngHAo0cP+Ouv\ngGz0gBAiuy7fCeXqvTAql7SidoUirz5A5FsajYY7fz/n/IkHREUkoDTQo1ajEtRqUJxFi2Zzcfc2\nnE4coHBMCiZ6EFizBM6ffo2NncwuE0II8fY0Gg3JycmUKlWaUqVK06VLNz77zJPg4CDc3Dw4etSP\nmJho3Nw8AHBycuLhwwckJydjaGiorefmzRtUqlRFe0vaf29PS9er11e4ubXh2DE/vv12IMuW6ZZJ\n/0ykS6FzETolJQWFIq3MixfYXyyTnc9CQoh3Sx6/kgNKly6rvZ3s4sULVKxYGSenYty6dQOAc+fO\nkJqaqnPMjh1biY6O4qOP2tCtW09u376JQqHIUK5SpSpcuJA2RdPf/wQ//6y7MF2RIraEhKQ9had2\n7XqcPn2SlJQUQkNDCAkJoVSpMtqySqWSkiVLcfnyJQCOHz9CgwaNiIiIYOTIb9FoNNy7dxe1Wo2N\nTRF+/30Xv/22HYB9+/bQpEkzbV3h4eHY2MiHXCHelCpVzZbD/6CnUNDTtbw8Sr2A0mg0PLwbxq8/\nBeD3+9/ERCXiXNuRnv0b0NClDEl3bvBRdCiTq9TCIjaFhxWtMRw3DBevKZIwEkII8c7s3bub2bOn\naxMucXGxqNVqrKysaNGiNefPn+Xy5Us0atQEAFPTQjRt6sKaNf97QNCxY4dZunShTtImM2q1mpUr\nl1GkSBG6d/8cZ+dqBAUFZfqZSKHQ036+qVy5ChcupF2EDg4OQk9P75W3w1WsWJmrVy+jUqkIDw9j\n3LiRb9ZBQohse29mGuWVR48e4uXVX7s9aNC3DBs2Urvom7m5OePHT0KpNGDfvt8ZOLAvtWrVwcLC\nUqceJ6fiTJgwFjMzMwwMDBg/fhKRkRH8+OMSnYWuXV3dCQg4h5dXf/T1lXh7++jUU7t2XbZu3US3\nbp/h4OBAhw6dGDz4axQKBSNHjkVPT0/nkZXffjuCOXNmoNGoqVLFWbvYXfnyFenbtxf6+nqMHu0N\nQLNmLnh7j+GPP/bi5FSMr78eCMDTp0+ws7OT9YyEeAuHAh4THJGAa51iONma5XU44g0EPo7kzPH7\nBD1Jm8FZoao99ZqV4v7Dm3z7aVsG16uN2bMQTIH7JQtR+OOOtK7uip5Crt8IIcT7LrdvLWnbtgMP\nHz6gf/8vMDExRaVSMWzYKIyMjDEyMsba2hoLC0udWUVDh45g+fLF9O7dDXNzC+zs7JkxY47OhazM\nZhnp6elhalqIAQO+wszMDEdHJ8qXr5DpZyIjI2NUqhS8vcfg4zOdixf/YsiQAahUKYwaNf6V51W0\nqCPu7m3x8uqPRqNhwIDB76bDxAcnqzuR8qOhedi2QvOq1HE+kVO3jwHY2prnaP0A0dFRXLgQQIsW\nrQkJec7QoQPZtGlHjrTVv/+XTJ06E3t7hxyp/78WL55H1arVad3aLVvlc6O/hS7p89z1uv0dFZvE\nuFVnUOrr8cOAhhQylidlvY68fn+HBsdy7s97PLwbDkDJcjY0aF4aGzszkh4/5vq6FZg9fgbAw6KG\npHzUjJaNPDFRFsxFrnOyv21ts7fgqshdOfXzXjHzGAADx7bIkfpzSkGNG/L+9+WHxtbWHP+OXXKk\n7oK2Hktukfd47iqI/Z2ewF3U0y7rgvnM0E3PgZz7v5/VGExmGuUSU9NCHDnix6ZNG9Fo1AwZMjzH\n2ho1ahxLlsxn2rTZOdZGun/+ucXz58/59tvsJYyEEBltP36XxORUeruXk4RRARIVEc+5Ew+4cyPt\nj3jR4pY0dCmDQzFLTu7ZjePDe6RcuowZ8NTWgGdNK+Pa4nPsCxWsQYoQQog3J8kdIfKnZa1y/rPy\nu3R705d51rYkjXKJUqlkypQfcqWt8uUr5krC6H9tzcqVtoR4H919FoX/1SBK2JnRvIZjXocjsiEu\nJomAUw+5eTkQtVpDEXszGriUpnhpa1QREQTMmILN3buoFApCrJRcr1eUxi2706JIFVmrSgghhBBC\nFCiSNBJCiDyi1mjYdOgfAHq6VUBPTxIK+Un6LSgvY2llQv3mpSlbyRZVTDQh27YQedQPC1Uq4ZZK\nzle3oFKLj+lTohkGevLnVgghhBBCFDwyihVCiDxy+loQ9wOjqV/ZjgrFC+d1OOI1uHhUoGI1B0hK\n5OGmjcQcPYwRCqJN9ThbxxzzRo3pXb4dlkYWeR2qEEIIIYQQb0ySRkIIkQcSklRsP3YXQ6Uen7Ys\nl9fhiNekXDqeuy9sq4z0OO1ciOja5elSqTOlLUvkWWxCCCGEEEK8K5I0EkKIPLD31AOi4pLp1LQ0\n1hbGeR2OeIFGo+HhnbBsl/evUYjbla35uEpHGhStjZ5CLwejE0IIIYQQIvdI0ugt7dixDV/f/2fv\nTgOqqtYGjv/PORxmkDgsfIAAACAASURBVOmggAwCzrOigjMqjpnadb6NWlrJTd+0MtLSyjS7dssc\n0kwbLc1G0yxnRRHnOVRQEAERkHk8w34/WBQhgsgg+vy+XNh7rbWfveUS+zlrPWsz5ubmFBYWMGnS\nFOrXr8/s2TP59NOvi9spisLIkUNZteozLC2tWLz4Xc6dO4u5uQX29vZMnz6T+vUblHmdmTOfZ8GC\nd28rtp07txEc3O+2+hw4sJ+kpERGjBhZbtu8vDxmznyeN99cyLlzv7Ny5VLUag1BQd14/PEnS7Xf\nsWMb8+fPZf369Tg6ugFw6FDkTfstXryIM2dOo1KpmDp1Ot7ePrz00vPMn78IW1vb27onIe42ydfz\n+O1QPM72lgzsIjNS7ibJiVlE7IwhKT6zwn0cBw3hVZ8+WJlJ8k8IIURJ5dXHq6xnZvYut80/31OG\nDBnG999/U6XvKDdTmfeW8mzevBEbG1t69QoucXzIkL5s2rS9Sq8lhCjpvkkanX/y8TLP6X78tlJj\nJiUlsnHjD6xa9RlmZmbEx1/m7bffZMmSlZiZaYmNvYSPTyMATp48jre3D46OTrz99jzc3Nx46aVX\ngBvJlDlzwli+fHWZ17rdX7x6vZ5169bedtIoMLBrhduuXr2SoUOHY29vz/vv/5dFiz5Ap3MlNHQS\nvXr1oVEj3+K2x44d4cCBffj5NS4xxs36ZWSkc+VKPCtWrCE29hLz57/OihVrGDlyLCtXLuX551+6\nrXsS4m7z9fYLGE0KY/r4Y67V1HY4AshMzydy90ViolIA8PZ3rvBso+H+g6szNCGEEOK2lfWeUtXv\nKDdT1QkjgMGDh1b5mEKIirlvkkbVIScnh6KiQvR6PWZmZnh6erFkyUoA+vUbwPbtvzFx4mQAduzY\nSkjIQPLycjl4MIL1638sHqdPn3506tSlxNihoZOKx4K/sui//PIz3323HjMzLf7+TZg+/SViYqJ5\n9923UalUWFvbMGvWHD766ENiYqL5738XMG3aDBYunEdiYgJFRUU8+eTTdO4cyJgxwxk27CH27dtL\nUVER77+/jF27dnDxYgyhodP48stP2bVrOyqVmqefDqVDh4DieAoLC9m1azvPPPMfEhKuYGdnX/wp\nRFBQN44cOVgiadS0aTPat+9IaOik4mNl9cvIyKBHj94A+Pg0Ijs7i9zcHHr27M3y5R+Ql5eHtbV1\nVfwTClHjTsakcSImjWZeDnRsqqvtcO57+XlFHNkXx5ljiZhMCq5udgT29sWx8BqfRtd2dEIIIe4V\nFZkZVBEVnblU1nvKV199UaXvKO+99w5RUb9jNBoZMWIkgwcPLX5vOXQoksWLF+Hk5IKXlzcODg60\nb9+Rb775Go1Gw/nzUTz66AQiIyO4cOEczz47lZ49e7N9+1bWrfsSjUZD06bNmTZtBh9/vAIHBweG\nDfsXc+fO4tq1ZJo3b3HnD1QIUa57JmmU8s3XZB8+VKm+h596GqPRVOq4XUAndKPGltmvceMmNG/e\nklGjHiQoqBuBgd3o1SsYMzMz+vXrz/PPhzJx4mRMJhMREfuYPHkKCQlX8PLyRqMpObvAzs6uQrF+\n/fUXLFz4HvXrN2DTpp8oLCzg/ff/y7PPTqVly1asXfs533zzNePHP8LZs6eZMWMmv/zyM+bm5ixZ\nspLU1BRCQyfz9dffYTQa8fLyYfz4R3nttZc5/LfnFx9/mV27trNixSckJibwxReflEga/f77Gfz8\n/NFoNFy/noaDg2PxOUdHRxISEkrEbW1tU+peyuqXkZFB06bNio87ODiSlpaGjY0tzZo158yZk3Tq\nFFih5yXE3cRgNPHV9guoVDC+XxNUKlVth3Tf0uuNnDp8hWMHLlNUaMTewZLO3b1xzbpIxqfvceVy\nHH2Bq05mHGtmzaD9WbUdshBCCFFhZb2nVOU7SlZWJvv3h7N+/Y8YDAY2b95Y4vzy5R8we/br+Pk1\nZsqUp4oTUNHR5/nyyw2cOHGUuXNn8803P3HmzCm+/XYdAQGdWblyKWvWrMXa2poXX/w/jh49XDzm\noUMHMBgMrFixhjNnTrNhw7oqfGpCiJu5Z5JGtWX27NeJjb3EwYMRrF37GT/8sIHFiz9Ep3PFwcGR\nmJhosrIyadKk2R+JExUmU+kEFdyoEfTii9OAG79MQ0Mn4e7uQVjYa8Vt+vUbQFjYCwwYMIh+/QZg\nYWFJbOwlWrZsBUCHDgGsWbOyxBTOc+d+p337jgC4uOgwN9eSlXWjXkfbtu0B0Onqk5ubU9zn/Plz\ntGjRCrVaTcOGnsycObtErKmpKeh09W96H4pyO0+w/H7K3064urqSnJxcuQsIUcu2Hb5C8vU8+nTw\noKGr1OaqDSaTwrlTVzkUfonc7CIsrczo2sMT9/Tfyfrka5IzMkClQtOyJfu8FCIdUkClkqSREEKI\nOqes95SqfEfx9PRm5sznCQ7ux8CBQ0r0S05OokmTGx8EBwZ2xWg0AuDv3xhzc3OcnV3w9PTCysoK\nJycncnJyiI+/TMOGXsWrCtq378j581HFY166dInWrdsA0LJlKywsLKr0mQkhSrtnkka6UWNvOSvo\nVjWNAj76kJSU7Nu+pqIoFBUV4ePTCB+fRvzrX2P4979Hkpx8lQYN3AgJGcjOndvIzs4iJGQgAB4e\nHsTFxVJUVIS5uXnxWFFRZ2nWrEXxdM9/Tv380yOPPEFIyCB27drGc889w9KlJdsYDHrU6n/u3KMq\nkXjR6/Wo/tjd5++fJvy9jUajxmS6dfbnz1kSLi46rl//q/ZHSso1XFxcbtn3Vv3MzMxIS/vreGpq\naoXGE+JulplbxMb9l7CxNGN4D9/yO4gqpSgKly9e58Cui1xPyUVjpqZtaye8Uo+T/+VnpBcVoba0\nxKFff2Ja6lhz8We0dhbAjd9z7493LTXm0j4La/guhBBCiIq51XtKVb6jLFq0mHPnoti6dQtbtmzi\nf/9betN4/j67+u/vH/98F1GpSr6TGAz6fySGlOL3mD/7CCGql+wLfAd+/vlHFi6cV/zLKjc3B5PJ\nhKPjjSVXvXv35dChSE6cOE5QUDfgxjKt7t17sWrV8uJxdu3azpIl75X7S89kMrFixVJcXFwYO/Zh\nWrVqzdWrV2nUyI/Tp08CcOzYUZo2bY5KpS7O5jdv3qJ4Wmdy8lXUanW5y+GaNm3OqVMnMBgMXL+e\nxssvzyhx3sVFR0rKjRk/bm7u5ObmkpSUiMFgYP/+8AotHyurX+fOgezadWMXhHPnonBxcSle3paS\nkoKr681nOAlxN/t2dwz5hUZG9PTF1kpb2+HcV64lZfHTVyfY/M0prqfk4u9lSR+zk7h8/y55e3eg\nsbNDN3os1q/NZG2TLL5I2Yq5jSW6ZPvaDl0IIYSolFu9p1TVO0pSUiLffPM1TZs2IzR0GpmZJXce\ndXJyJi4uFqPRyKFDkRWK29PTmytXLpOXlwv8+W7zV+0iLy9voqLOAnDq1AmKiooq+ESEEJV1z8w0\nqg2DBw8lLi6WSZMew8rKGoPBwLRpL2BhcWPbZXt7e5ycnLC3r1ciYz916nSWLVvMo4+Owc7OHlfX\n+rz11jslMvA3m2WkVquxtrZh8uQnsLW1xd3dg8aNmzBt2oziQth2dnaEhb2GhYUlBoOeWbNeYs6c\neRw7doT//GcyBoOeF14IK/fe3NzcGTBgMKGhk1AUhcmTp5Q437x5S6KjL2A0GtFoNMyYMZM5c27s\ntNCnTwheXt6kpaXy8ccrePHFV/j55x/YsmUz0dHnefnll/Hw8GL27Ndv2g+8adq0OU8/PQGVSlW8\nW5qiKPz++9kKxS/E3eRSUhb7TibRUGdDr3butR3OfSMrI5/IPZeIPnsNAHdH8L16AIsdUZgASz9/\nHEMGoG3dknc2Lybp+HZUGhWtXZozqvEwnK2cmLLjxdq9CSGEEPeEihawriq3ek+xsLCskncUFxcd\np0+fYPv239BqtQwZ8mCJGJ566lleeeUF3Nzc8fb2KVUv6WasrKyYMmUq06f/B5VKTZs27Wjbth2H\nD99IOgUGdmPTpp8IDZ2Ev39jdLrSM4GFEFVLpdSROX2VWT5WUTqdXbWOf6/64IN3adGiFX379r+t\nfpV93nv37iIy8gAzZsy87b73O/kZr1l/f96KovDWF0eIScjixXHtaebtWE5vcbv++fNdkK/nyL44\nTh9NwGRScLQ04JsUgUNaDKjV2HYIwDGkP1Z+/pxKPcs3538krSCd/LRcxjQdzoA2IbV4N3e/6vx9\notNVbFMIUbOq69/7z5foqtpVqqbU1bhB/h6oaTqdHa9P31h+w0qoCz9/Bw8ewNPTCzc3dxYunEe7\ndh3p339gtV5TfsZrVl183n+WrWmy6pNajeN2VXfct/obTGYaiUqbOHEyL788g06dArG3r95lHHl5\nuaxf/xXz5r1TrdcRoqodOJNMTEIWAc1cJWFUzQx6I6eOJHA0Io6iQiPWGj2+KZG4ZkajsbKi3oCB\nOPQJQevsTHLWNf63cxEJSjJqlZquzp3o2TwQTzfP2r4NIYQQ95C6kNypLoqiEBY2A2trGxwdnQgO\n7lvbIQkhKkGSRqLSrK1teP/95eU3rKJrffDBihq5lhBVpaDIwDe7otGaqRkd7Ffb4dyzTCaFqFNX\nObjnErnZhWgx0DjlKA0zo7DQOeMw9t/U694dtaUVRpORrXG7+D7qZ1RaNW5aHRPaP4K7bYPavg0h\nhBDintKlSxBdugTVdhhCiDskSSMhhKgmmyLiyMgp4sFuPrjUs6rtcO45iqIQfymdbz85zLWrOagV\nI94ZZ/FOP4W9nw+OD0/Bpm17VH/sKBmdcYl1574nMfcqlloLjCfyCH3iSRxsZQaYEEIIIcT9YLv/\n4zf+t4brjN2xP+JuUguXlqSREEJUg+T0PH49eBknewsGBXrXdjj3nJSr2URsO0/ClWxQFNyyY/DN\nOIGufSscQ8Kw9PEpbptTlMvyfauIVRIA6ObemQf9BmHbx6aWohdCCCGEEKJukKSREEJUg3XbozEY\nFUYH+2OhLX+3EFEx2ZkFRGw5TcylHACcchNomn8Wj6B2OPR5Ha3jX7OGTIqJiKRD/Bj9C7lKHhlx\naQxu0JfxzUbWVvhCCCGEEOIuUNfqjdX0Dox/J0kjIYSoYkfPXeN4dCpNPR3o1Ey2gq0KBXmFRG48\nStSlfEyosS1Mo5kxhsZ9OuL74ONcz9aXaH8lO5GPDn9KqpKOhcacYY0G4ebqTOtWbWrpDoQQQggh\nhKh7JGl0h779dj2//roZc3NzCgsLmDRpCvXr12f27Jl8+unXxe0URWHkyKGsWvUZlpZWLF78LufO\nncXc3AJ7e3umT59J/fq3V4h15sznWbDg3Sq9n82bN2JjY0uvXsEljg8Z0pdNm7aXar9ixVL8/ZvQ\npUsQc+e+Qk5ODlZW1syZ8yb29vVKtF279jN27tyGVmvGI49MICioO2lpqcybN5fCwgIcHR0JC5uD\ntbU1e/fu4tNPV6PVaunXrz//+tcYlix5jzZt2tGzZ+8qvWchqpLBaGLVj6dQqWBcv8aoVKraDqlO\nK8rN58iPEZyOM2JQabHU59HM4iqthnXEts0IVGo1GktL+CNpVGAoYNOlrey4vBdU4FLowLTgZ3G0\ndKjlOxFCCCGEEKLuqdak0VtvvcWJEydQqVSEhYXRps1fn/B++eWX/PTTT6jValq1asUrr7xSnaHc\ncjrXq4uGVmrMpKRENm78gVWrPsPMzIz4+Mu8/fabLFmyEjMzLbGxl/DxaQTAyZPH8fb2wdHRibff\nnoebmxsvvXTjnnfs2MacOWEsX776tq5f1QkjgMGDK/4soqMvcO5cFJMnT2H16pW0b9+R8eMf5ccf\nv+OLLz7l2WefK26bmJjAtm2/sWLFGiwtYcyYsXTuHMTnn39Cjx69GDFiJFu2bGLDhq95+OHH+d//\n3uHjj7+gXr16zJjxHD169Oapp55h0qTH6dw5EEtLyyq/dyGqwo6jCcQn5xDc3gOv+na1HU6dpU+/\nzskf93HyipoCMxvMTHpa1Uuh/ZAAbBs9UKq9oigcTznNhgs/kVGYiYPWniubzjN98mRJGAkhhBBC\nCFFJ1ZY0OnjwIHFxcaxbt46YmBjCwsJYt24dADk5OXz88cf89ttvmJmZMWHCBI4fP067du2qK5xq\nkZOTQ1FRIXq9HjMzMzw9vViyZCUA/foNYPv235g4cTIAO3ZsJSRkIHl5uRw8GMH69T8Wj9OnTz86\ndepSYuzQ0EnFYwG89947REX9jtFoZMSIkQwePLR49s+hQ5EsXrwIJycXvLy8cXBwoH37jnzzzddo\nNBrOn4/i0UcnEBkZwYUL53j22an07Nmb7du3sm7dl2g0Gpo2bc60aTP4+OMVODg4MGzYv5g7dxbX\nriXTvHmLm97/hg1fM3z4vwA4cuQQL7/8KgDduvXkxRenlWh79OhhAgO7otVqcXKyo0EDN2JjL3Hl\nymUGDhwC3NiWc/bsmQwdOhxbW1sc/6hN0rFjJw4fPsjgwUPp1q0HW7duYejQ4ZX+dxOiumTlFfFj\n+CVsrbSM6Olb2+HUSQWX4zi3aS+nrlmRbeGMSmOkiVM+XR7shG0Dl5v2Sc5J4d2Ij7hUEI8aNYN8\n+tLfuw/mPbU1HL0QQgghhBD3lmpLGkVERNCvXz8A/Pz8yMzMJCcnB1tbW7RaLVqtlry8PKytrcnP\nz6devXrljHhr+3fEcDHqWqX6vv/mNkxGU6njvs1c6drHr8x+jRs3oXnzlowa9SBBQd0IDOxGr17B\nmJmZ0a9ff55/PpSJEydjMpmIiNjH5MlTSEi4gpeXNxpNycK4dnZlz0jIyspk//5w1q//EYPBwObN\nG0ucX778A2bPfh0/v8ZMmfJUcQIqOvo8X365gRMnjjJ37my++eYnzpw5xbffriMgoDMrVy5lzZq1\nWFtb8+KL/8fRo4eLxzx06AAGg4EVK9Zw5sxpNmxYVyquI0cOF88mSktLw8HhRpLH0dGRtLTUEm2v\nX//r/N/b+Pr6ExERTrNmzTlwYD8ZGek4ODiSl5dHfPxl3NzcOXr0CO3bdwCgXbsO/PLLz5I0Enel\n73ZfJL/QwOQRrbG1koRFRSkmE7knTxD36x5O57hw3aYhWICPC3Qd1ol6Ovub9tObDGy/vJstcTvQ\nG/Ukn7pCe5rzQJ8BNXwHQgghhBBC3JuqLWmUmppKy5Yti793cnIiJSUFW1tbLCwsmDJlCv369cPC\nwoIhQ4bQqFGj6gqlWs2e/TqxsZc4eDCCtWs/44cfNrB48YfodK44ODgSExNNVlYmTZo0w9raBlBh\nMpVOUAHk5eUVz9CJjj5PaOgk3N09CAt7DU9Pb2bOfJ7g4H7FM3P+lJycRJMmzQAIDOyK0WgEwN+/\nMebm5jg7u+Dp6YWVlRVOTk7k5OQQH3+Zhg29sLa2BqB9+46cPx9VPOalS5do3frGcsKWLVthYWFR\nKt6cnOxSdYvgxjKR8vzZ5JFHnuC//51PaOgkgoK6oSgKKpWKV16Zw/z5r2Nra4ubm3txe53OlWvX\nkssdX4iaFnc1m70nEvHQ2TAoyIfr13NrO6S7nqmggMz94SRt38t5xZMkuzZgo6KBixndBrfG1b3s\nDxPOXY/m05NfkWnKxsHSnhFNR6LYFZWatSmEEEIIIYSovBorhP33REJOTg4rVqxgy5Yt2Nra8thj\njxEVFUWzZs3K7O/oaI2ZWdnbVg8bc+ulba9P31jmuamz+t2yb1kURaGoqIhOndrQqVMbnn76SQYN\nGoRen42HhwcPPTScyMg9ZGVlMXLkCHQ6O6ysmhIfH0e9ehaYm5sXj3Xq1Clat27NunVfAfDII4/w\n+eefF5//7LM1nDlzhp9//pnZs19g9erVqFQqdDq74v8FsLW1xGAw4OBgjbW1JTqdHenpNlhZWRR/\nrdVqcHKyQatVF/ezsFD/kRgyYGtriZkZqNV/nVcUpfjrP2k0f513d2+AouSj07mRkJBAgwb1S7Rv\n1MiTS5cuFR/LyEijcWNvfH3dWbbsAwAuXrzIqVPH0Ons6N+/N/379wZg0aJFNG3qi05nR2bmjfj/\nGYsonzyz6qMoCu98fRwFeOZfbUv8f0OUVpiSStLmX4j/bRcXLfyId+iJSaXBxdmS/g+1xa+prswC\n4hkFWXx2/FvC4w6imBTsUrS89/QcrM2toHUN38h9TH6+a8bChQs5cuQIBoOByZMns2PHDs6cOYOD\nw406XRMnTqR379789NNPfPrpp6jVakaPHs2oUaPQ6/XMnDmTxMRENBoN8+fPx9PTk6ioKObMmQNA\n06ZNmTt3bi3eoRBCCCHudtWWNHJ1dSU19a8lSteuXUOn0wEQExODp6cnTk5OAAQEBHD69OlbJo3S\n0/OqK1QAUlKyb7vPxo0/cPz4UWbNmotKpSIrKxO93oCimJOSkk3Hjt144YWpFBYWMmHCs8XX6Nq1\nJ/PnL+TZZ6cCsGvXdjZsWMcHH6woflEqKjIUt09KSiQ8fA+jRo1lwoRnmTDhYVJSslEUhZSUbBwd\nnTh8+BQNG3qya9ce2rfvSEZGHoWFelJSsklPzy0e78+vbW1duHjxEnFxV7G2tiE8PILHHpvI4cOR\naLUF1K/fgK1bf2Xo0GxOnTpBUVFRqWdkZWVDTEwC9vb2tGsXwLff/sjjjz/Jd9/9RMeOXUq0b9y4\nNatWfcy4cU+g0ehJTLxKvXr1+fjjzzCZjAwfPpIvvviaTp26kpKSzfTpzzFr1hwsLa3YunUbDz44\nmpSUbM6fv4SDg3Ol/r3uZzqdnTyzanTgzFV+j71OxyY63B1uFGm/n5/3+ScfL/56u//jZbRyQOs6\nCL3aAhsbLZ17+9GkZX3UahWpqTmlWpsUE+EJB/jp4hbyDQU0tHHn9OeR/Hvcs1ibW93Xz7umVefv\nE0lG/eXAgQNcuHCBdevWkZ6ezogRIwgMDOT5558nOPivHU7z8vJYunQpGzZsQKvVMnLkSEJCQti5\ncyf29vYsWrSI8PBwFi1axHvvvce8efOKNyeZPn06u3fvplevXrV4p0IIIYS4m1Vb0qhbt2588MEH\njB07ljNnzuDq6oqtrS0AHh4exMTEUFBQgKWlJadPn66Tf7AMHjyUuLhYJk16DCsrawwGA9OmvYCF\nxY2XRnt7e5ycnLC3r1diVtHUqdNZtmwxjz46Bjs7e1xd6/PWW++U+GT970WwXVx0nD59gu3bf0Or\n1TJkyIMl4njqqWd55ZUXcHNzx9vbp1S9pJuxsrJiypSpTJ/+H1QqNW3atKNt23YcPhwJQGBgNzZt\n+onQ0En4+zdGp3MtNUaHDh05efIY3bv3YuTIsbzxxmyeffZJbG3tePXVNwB4//1FjBo1Fnd3D4YO\nHc6UKU9hbm7GjBkzUavV9OjRi1mzXmLz5p/x8GjIU089A8CDDw7n//4vFJXqxhK2Pz9VPX78GB06\nBFTo30eImlBYZOSbXTGYadSM7uNf2+HUKSpLawK7etO6owdm2rJ/b13OusIXZ9eTkHcVM0XDmKbD\n6e4RiLqLugajFaJmderUqXjXWXt7e/Lz84uXn//diRMnaN26dXFtxA4dOnD06FEiIiIYPvxG/b+u\nXbsSFhZGUVERCQkJxeMGBwcTERFRJ/8GE0IIIUTNqLakUYcOHWjZsiVjx45FpVLx2muv8d1332Fn\nZ0dISAgTJ07k0UcfRaPR0L59ewICqjcR8MzM3lU+pkajITR02i3bzJ+/qNQxrVbL1KnTK3wdrVbL\n3LnzSx3ftGk7AJaWlrzzzvu4ubmzcOE83N0b0qFDQHFyxdfXvzgJ9feve/XqQ69efUqM+edub/+M\nfdq0F0pdf+TIMaxYsZTu3XthbW1903v9+32OHDmWkSPHlviU2tHRiaVLPyrV72axFRYWEh6+hw8/\nXF2qvRC1ZdOBWNKzC3mgqw86B6vaDqdOGf90F6yszcs8n2/IZ+PFX9lzJQIFhSv7Y7C9ZEbPj7vW\nYJRC1A6NRlNcd3DDhg307NkTjUbDF198wZo1a3B2dmb27NmkpqYWz9yGv2pI/v24Wq1GpVKRmpqK\nvf1fheWdnZ1JSUmp2RsTQgghRJ1SrTWNZsyYUeL7vy8/Gzt2LGPHjq3Oy983FEUhLGwG1tY2ODo6\nERzct0au27hxU/z9m7Bz5zaCgytXF+p2rFr1IRMmPIWVlbyYi7vDtYx8tkTG42hnwZBA79oOp9aZ\nCvLJOXa0wu3LShgpisKR5ON8c+5Hcox51LfWMabJCLLtr9OuXYeqCleIOmHbtm1s2LCB1atXc/r0\naRwcHGjevDkrV65kyZIltG/fvkT7sjajuNnximxcAeXXlbxTdXVZosQtKkKed82TZ16z6urzlrgr\nrsYKYYvq06VLEF26BNXKtZ9+OrTGrjVlytQau5YQFbF+RzQGo4nRwf5YmFffC9XdzKTXk3f6FFmR\nB8g9eRylqAgAvdqcWMc2tz1ecl4K6859z7n0aIxFBvKOXud/L83DXKMFp/L7C3Ev2bt3Lx9++CGr\nVq3Czs6OoKC//lvfp08f5syZw4ABA0rVkGzXrh2urq6kpKTQrFkz9Hr9Hxta6MjIyChum5ycjKtr\n6eXn/3Q31pW8G9TFuKXGYc2S513z5JnXrLr8vCXukm6VjJKCEEIIUQlnY69z9HwKTRrWo3Pz8l+6\n7iWKyURe1O9c/XQ1F6dPJXHpYnIOH8TM0QnHB0cQX68ZEd4PcdmxVYXHLDLq+fnir7wV+S7n0qNp\n6dQM9fZc+nsFo1XL5xvi/pOdnc3ChQtZsWJFcV2///znP8THxwMQGRlJ48aNadu2LadOnSIrK4vc\n3FyOHj1KQEAA3bp1Y8uWLQDs3LmTLl26oNVq8fX15fDhwwD89ttv9OjRo3ZuUAghhBB1gvwlLoQQ\nt8loMvHVtguogHH9mpS5Pfy9RFEUCuPiyI6MIOtQJMY/ZitoHBxw7N4Tuy6BJOtt2b7zIum6emhM\nRfilHiHGpWO5sClcRgAAIABJREFUY59JO8e6c9+TVnAdsyI1T3V4hLa6VqjaTaju2xLirrV582bS\n09OZNu2v2okPPfQQ06ZNw8rK6o9agvOxtLRk+vTpTJw4EZVKxZQpU7Czs2Pw4MHs37+fcePGYW5u\nzoIFCwAICwvj1VdfxWQy0bZtW7p2lRphQgghhCibJI2EEOI27TyaQEJqLr3auePdoG6uh66ooqtX\nyYqMIPvgAfTJyQCora2p17MXdp0DsWrSlPS0fLbtiCb+UiwqFbRo50anHo2wtul/y7EzCjPZcP4n\njqWcQo2KxF0xxG+9wIIdr90XiTghbmXMmDGMGTOm1PERI0aUOjZw4EAGDhxY4phGo2H+/NKbaPj7\n+7N27dqqC1QIIYQQ9zRJGgkhxG3Iyivih72XsLIwY0RP39oOp1ro09PJORRJVuQBCuNiAVCZm2PX\nqTN2XYKwbtkKtVZLfl4Re7ZG8/vxRBQFGvo40rWPH86utrcc32gysvvKPjZe/I0iUxGN7L0Z1+wh\nrutS8JruJcXuhRBCCCGEuEtI0kgIIW7DD3sukldoYFzfxtjfYrv4usaYm0vOkcNkHTxA/rkoUBRQ\nq7Fp3Qa7zoHYtm+P2vJGMsdoMHEs8jJH98dRVGjEwcmKoD5+ePs5l5ohNGXHi2Ve05CnJ+7n35n/\nzizsbe3xaO1WrfcohBBCCCGEuD2SNBJCiAq6nJzN7uOJuLvYENzBo7bDuWOmwkJyTxwn6+ABck+d\nBKMRAKvGTW4kigICMLOzL26vKAoXz6VyYFcMWRkFWFia0a2fPy3bu6PR3P6+Co3Ou+Dh3hmtmbbK\n7kkIIYQQQghRdSRpJIQQFaAoCmu3nkcBxvVtjFklkiR3A8VgIO/3s2RFRpBz7BhKYQEA5g09se8S\niF3nLmidXUr1S7mazb7t0STFZ6JWq2gT0JCO3byxtKp8wmfm869Uuq8QQgghhBCi+knSSAghKuBQ\n1DXOX8mkfWMXWjZyqu1wbotiMlEQE0PWwQhyDh3CmJMNgJmLC/Z9+2HXJRALj4Y37ZuTXcjB3Rc5\nd/pGEWwff2eC+vjh4GRd5vVMiolTqb+zM35v1d+MEEIIIYQQosZI0kgIIcpRqDeyfmc0Zho1Y/o2\nru1wKqzwSjxZkQfIPngAQ1oaABo7exz63EgUWfr6lblLmV5v5HhkPMcjL2PQm3DW2dC1rz8NfRzL\nvF6+oYADSYfZFR9OasH1arknIYQQQgghRM2RpJEQQpTjlwNxXM8qZEiQN64Od/fOXvqUFLL/2Pms\nKOEKAGpLS+y7drux81mz5qg0mjL7K4rC+TPJRO6+SG52EVY2Wrr186dZazfU6psnmFLy0th9ZR8R\nSYcoMBaiVZvRzb0zOz/ajPmQ0kvdhBBCCCGEEHWDJI2EEOIWUjPz+SXyMg625gwJ8q7tcG7KkJVF\n9uGDZEceoCAmGgCVmRm27Tti1yUQmzZtUZuXv9NbUnwG+3fEcC0pG41GRfsgLzoEemFuUfo/FYqi\ncCHjIjvjwzmVehYFhXrm9vT3DqabexdszW2w661lC/uq/H6FEEIIIYQQNUOSRkIIcQvrd0SjN5gY\nFeyPpXnN/so8/+TjZZ7z+2A5uceOkhUZQd7vZ8FkApUK6+YtsOsSiG2HjmisbSp0nayMfA7sukhM\nVAoA/s1dCezti109y1Jt9UY9h5OPs/NKOAk5SQB423nSx7M76iQjH8x9j57LggAYOnQYQxl2m3ct\nhBBCCCGEuFtI0kgIIcrwe1w6h8+l4O9Rj8AW9Ws7nBIuPv8cil4PgGUjX+w6d8GuUxfMHBwqPEZR\noYGjEXGcOHQFk1HB1d2Obn39aeBRr1TbzMJs9iZEsDchghx9LmqVmg6ubQj27EEjey9UKhWvLgtj\n06af2LZtFEOHSrJICCGEEEKIuk6SRkIIcRNGk4m1286jAsaHNC6zYHRtMXN2xr5LEHadAzGvf3sJ\nLZNJIepkEgf3XCI/T4+tvQVdevnSuIVrqfu8nH2FXfH7OJx8HKNixNrMihCv3vRq2BVHSwfOnDmN\nquWNPi+99AoDBw6ma9fuVXafQgghhBBCiNojSSMhhLiJXccSSUjJpUcbN3wa2Nd2OKX4vDG/Uoms\n+EvX2b8jhuspuZhp1XTu4UObzp5otX8VxzYpJk6mnGFHfDgxmZcAqG/tSrBnNzo36IiF5kZ9pA8+\neI833niVzz77moEDB2NjYyMJIyGEEEIIIe4hkjQSQoh/yMnX88Pei1hZaPhXL79aiUFRlFuev92E\nUXpaLhE7YoiLuQ5AszYN6NyzETa2FsVt8vT57E86yJ4r+0krSAeguVMTgj170NypMWqVusSY/fsP\nZPPmjXh4eNxWLEIIIYQQQoi6QZJGQgjxD9/vvUhugYGxffyxtyl/17GqZtLrufbl51UyVkG+nsPh\nsZw5lojJpODu5UDXPn7oGtgVt7mWl8KuK/uISDpMkbEIrVpLd49Aght2o4HNX0vfkpISee21MGbN\nmouXlzdNmzZj8+Ztd93SPSGEEEIIIUTVkKSREEL8Tfy1HHYdS8DN2Zo+HRvW+PUNmRkkLltCQUz0\nHY1jNJo4fTSBI/viKCwwUM/RiqBgX3wau6BSqVAUhXPp0eyMD+dMWhQKCg4W9Rjk05du7l2w0VqX\nGjM8fA8//PAd3t6NeOWV14Dbn/EkhBBCCCGEqDskaSSEEH9QFIWvtp1HUWBc38aYadTld6pCBZcu\nkrjsAwzp6dh1CaT+o0+gtrAov+PfKIpC7IU0InbGkJmej7mFhq59/GjV0QONRk2RUc+h5KPsit9H\nYu5VABrZexPs2Y12utZo1JoS4128GE3Dhl6Ym5szcuQY6tWrR0jIwCq7ZyGEEEIIIcTdS5JGQgjx\nhyPnUoi6nEE7fxda+TrX6LWzIvaR/OkaFKMRl5GjcRww6LZn8aQm57B/RzQJcRmoVNCqgzsB3X2w\nsjYnozCTvbER7E08QK4+D7VKTUD9dvRu2J1G9bxuOt6OHVt57LHxTJs2g+nTX0KlUtG//6CquF0h\nhBBCCCFEHVCppNETTzzBmjVrqjoWIYSoNYV6I+t2XMBMo2JMX/8au65iNJL67Tek/7YFtZUV7lOe\nw6Z1m9saIy+nkMg9l4g6eWPmkJefE0HBfji52BCXFc/OM+EcuXYCk2LCxsya/t7B9PQIwtHS4Zbj\nBgR0pnnzFjRr1qLS9yeEEEIIIYSou8pMGsXHx5fZKS8vr1qCEUKI2rIl8jJpWYUMCvSivmPpej7V\nwZibS9LK5eSdOY22QQM8Qqdh3qBBhfsb9EZOHLrCsQOX0RcZcXSxpltff9y963Ei9QxrjuzlYmYc\nAA1s6tOnYXc6NWiPuebmxb1zc3NZuPAtgoP70rt3H+zt6/Hrr7ukbpEQQgghhBD3qTKTRoMGDcLV\n1fWm59LS0qotICGEqGlpmQX8ciCOerbmPBDkUyPXLExMIHHJYvTXkrFp05YGT05GY112smr5gl1l\nnrO00hI0wBfvFg5EXD3Eioj9pBdmANDSuRnBnt1p5ti43OTPxYsxrFy5jLNnT9O7dx9ACl0LIYQQ\nQghxPyszafTcc8+hKAqTJ08ude6RRx6p1qCEEKImrd8ZTZHBxKO9/bCyqP5SbznHj5H00QqUwgKc\nBj+A8/CHUKkrX3S73yO+7Es5wKqIwxSZ9JirtfT0CKJ3w27Ut7l58v9P6enXMRiM6HQ6Wrduwxdf\nrKNr1x6VjkUIUZqiKJKAFUIIIUSdVObb0aRJk1ixYgW5ubnY2NiUOOfvX3P1PoQQojqdu5zOoahr\n+LnbE9iy4kvDKkNRFK5v2kjaD9+hMjfHbdIz2HXucsfjLjj+PwAcLRwY4tmNrm6dsNaWv8QuNvYS\nQ4aE0KlTFz755EsA+vbtf8fxCCFKCg4OZtiwYYwcORJPT8/aDkcIIYQQosJu+ZH6zWYZAbz22mvV\nEowQQtQkk0lh7bYLAIwPaYK6GmcCmAoKuLpmFTlHDmPm5Ix76HNYenmX26+wQM/RiMu3bONXz4dg\nzx60cWmBRq2pcExeXt60bNmKDh0CMJlMqO9gtpMQomzffPMNv/76K2FhYZiZmfHQQw8xYMAAzM1v\nXl9MCCGEEOJuUf3rMIQQ4i61+0Qi8ddy6N7ajUZu9tV2HX1KCglLF1N0JR6rxk1weyYUM/tbX89o\nNHHmWCKHw2MpLDDcsu3zHZ+tUBwmk4nPP/8Eo9HIhAlPoVarWbfue1k2I0Q10+l0PPzwwzz88MPE\nxcXx8ssv8+abbzJ27FieffZZLCwsajtEIYQQQoibko+VhRD3pdwCPd/vuYiluYZ/9fKttuvkRf1O\n3Ly5FF2Jp17vPjSc/uItE0aKonDxXArrVh1i37ZoFJOCd6eq2c0tKyuTt99+k//97x0KCgoAKXQt\nRE05dOgQL7/8Mk899RQdOnRg7dq12NvbM3Xq1NoOTQghhBCiTDLTSAhx35iwYMdNj//fkn2sntmn\nSq+lKAoZO7eT8vVaUKlwfeQxHHoF37JPcmIW+3fEcPVKJioVeLWy55zzIY7mx9CKwZWKQ6/Xc/Vq\nEp6eXjg4OPLxx5/TqJEvlpaWlRpPCHH7QkJC8PDwYPTo0bz++utotVoA/Pz82LZtWy1HJ4QQJZX1\n91JdUdV/0wlxvys3abR7924yMjIYNmwY06dP59SpU8yYMYP+/aVYqhBC3IxJryf50zVkhe9BY2eH\n2zOhWDdpWmb7rIx8IndfIvr3awC4+9qT4nWezXmbIR9au7TgVOfNt7hi75seLSwsZPDgfhQU5LN9\neziWlpYEBXW7gzsTQlTGsGHDCA0NLXHsq6++Yty4caxdu7aWohJCCCGEKF+5SaNly5axfPlydu/e\njclk4vvvv+fpp5+WpJEQQtyEITOD0+8sJ/vcOSy8vHGf8hxaZ+ebtv2zyPXJw1cwGRWc6ltjbJLC\n9qJfMeYZ8bb3ZITfEBo7+jJlx4u3HYuFhQVdugSSn5+PwaAHZHaREDXp7NmznDlzhk2bNtGgwV+7\nM+r1epYuXcq4ceNkiagQ4q5V12bs1PUZUkLcrcpNGllaWuLk5MTu3bsZNmwYNjY2ssOOEELcRMGl\niyQu+wBDejp2nQOp/9gTqG9S4NZoNHH2WCKH98VSkG/Axt4Cm5YFRPAz+YUFOFs6McxvIB1c2xa/\nUC7ts7BCMezZs4s9e3Yxa9YcAN588235nS1ELbGwsCAtLY3s7GyOHDlSfFylUvHii7efCBZC1B2S\nwBBC3CvKTRoVFhayatUq9u7dy0svvURsbCzZ2dk1EZsQQtQZWRH7Sf50NYrRiPdjj2DevU+pGQSK\nonDpfCoHdl0kMz0fc3MN7h3MOWS5m3TDdWzMrPlX46H08AhCq779knOKovDWW3M5ceI448b9Gz+/\nxpIwEqIW+fn54efnR2BgIO3atavtcIQQQgghblu5byVvvPEG69evZ/78+VhYWBAeHs6MGTNqIjYh\nhLjrKSYTqd+uJ/3XLaitrHB79j807NudlJSSyfVrSVns3x5D0h9Frj1a2HDW6QBHi+IxM5nRz6sX\nA7yDsdbe3k5piqIQH38ZLy9vVCoV7723jKKiQvz8GlflbQohKuHNN99k1qxZLFy48KbL0L788sta\niEoIUZPq2hIvIYT4p3KTRj4+PkyYMAE3NzeioqKwtbWlffv2NRGbEELc1Yy5uSStXE7emdNoGzTA\nI3Qq5g3cSrTJysgncs8los/eKHLdoJEtiR5n+bXoNBRBp/odGOo7AGcrx0rFMHXqs/z880/s3RuJ\nh0dDmjVrfsf3JYSoGiNHjgRg2rRptRyJEEIIIUTllJs0mjlzJiEhIajVav7zn/8QEhLCzp07ef/9\n92siPiGEqDKThrZg5cazDO/eiAe7N7qjsQoTE0hcshj9tWRsWrehwVNPo7H+a5bQn0WuTx2+gtGo\n4ORqRV7jRLbrf0EpUmji6M8I/8F42TW8ozi6dAni8uU4DAbDHY0jhKh66enpRERE1HYYQgghhBCV\nVm7SKDk5mYEDB7JmzRrGjx/PE088weOPP14DoQkhRNU6FHVjtk9AM9c7Gifn+DGurlqBqaAAp8EP\n4Dz8IVR/1A4yGk0c3HuJXb9G3ShybWeORfMcIlRb0ev1uNs0YLj/YFo4Na3UrkkxMRdYvnwp8+e/\ng1arZfz4Rxg37mGpXSTEXWjZsmVlnlOpVAQFBdVgNEIIIYQQt6/cpFFRURGKorB161bmzZsHQG5u\nbrUHJoQQVSm/0MCpi9fx0Nng7mJTqTEUReH6po2k/fg9Kq2WBpOexr5zYPG52AupROy8UeRaa67B\ntZ2GQ5bbyTZlU09rxwO+wwh0C0CtqnyCZ9myJXz++Rp69uzFgw+OQKVSyZbdQtylPv/889oOQQgh\nhBDijpSbNOrcuTMdO3akR48eNGrUiE8++QRfX9+aiE0IIarMiehUDEYTnZpWbpaRqbCQq2tWkXP4\nEGZOTrhPeQ5Lbx/gjyLXO2JIir9R5NqrrS1HbPZwzHAVC5U5DzQaQB+vHlhozCt17YSEK3h43FjG\nNnv2HIKD+zJkyNBKjSWEqDl/FsIeP368FMIWQgghRJ1UbtJoxowZTJo0CXt7ewD69u1Lq1atqj0w\nIYSoSneyNE2fmkLCksUUXYnHqnET3J4JxczenuzMAiJ3X+TCH0WudT7WXHY/yWbDedRGNT08ghjc\nqB/25naVjnvVqg959dUwNmz4ia5du+Pg4MgDDzxY6fGEEDVHCmELIYQQoq4rN2mUk5PDxo0bSU9P\nB0Cv1/Ptt98SHh5e7cEJIURVKF6a5nL7S9Pyon4n8cOlmHJyqNcrGNdx/6bIAId2xhQXuXbQWZLt\nd5mdpsNggE4ebRnUMIT6NndWOwmgQ4cAvL19UKs1dzyWEKJmNWvWDIBOnTqxZ88eLly4gEqlokmT\nJvTo0aOWoxNCCCGEKF+5SaNp06bh7u5OeHg4AwYMYN++fcyZM6cGQhNCiKrx59K025llpCgKmTu3\nc+3rtaBS4frwo9j16M3p44kcDo+jIF+PtZ056qYZ7FdvwWQy4WPvxQj/IQQ1bkNKSnalYs3ISGfB\ngjd5/vmXcHV1pUOHAMLDD6HRSNJIiLrqhRde4OrVq7Rr1w5FUfjwww/ZvHkz8+fPr+3QhBBCCCFu\nqdykUWFhIa+//jqPPPIIL730EhkZGbzxxhv069evJuITQog7drtL00x6PdfWfk7W3j1o7Oxo8PQU\nrqlc2PzxITKv3yhy7dRG4ZDlb+STh4ulE8P8B9Ne1/qOi1L/+OP3rF79ETY2tsyePRdAEkZC1HGx\nsbFs2LCh+HtFURg9enQtRiSEEEIIUTHlJo30ej15eXmYTCbS09NxdHQkPj6+JmITQog79velaR4V\nWJpmyMwgcdkSCmKisfDyRjv6SbYeTiUp/jQqFbg01XLSYR/XlVRszKwZ2ehBengEYqYu99dpmZKT\nk3FxcUGj0fDww49hbm7OyJFjKj2eEOLu4u7uTn5+PlZWVsCND+S8vLxqOSohhBBCiPKV+5YzbNgw\n1q9fz6hRoxg8eDBOTk7yh44Qos44EVPxpWkFsZdIXLoYQ3o6mo7dOO8WRPQPMQA4e1lw0e0Yp5RY\nzFRmhHj2pr93MNZaqzuKb8+eXTzxxMPMnPkKTz31DBqNhnHjHr6jMYUQd4cXXngBlUpFfn4+ISEh\ntGvXDrVazYkTJyq0qcjChQs5cuQIBoOByZMn07p1a1588UWMRiM6nY533nkHc3NzfvrpJz799FPU\najWjR49m1KhR6PV6Zs6cSWJiIhqNhvnz5+Pp6UlUVFRxmYGmTZsyd+7can4KQgghhKjLyk0ajRs3\nrvjroKAg0tLSaNGiRbUGJYQQVeVwVApQ/tK0rIj9JH+2Br1JRXLQeC5ct8AYlUo9nQXXfS6yW3UC\nlaKic4MODPUdgJOlY5XE17x5S5ycnLCzs6+S8YQQd4+uXbsWfz148ODir4ODg8tdynrgwAEuXLjA\nunXrSE9PZ8SIEQQFBTF+/HgGDRrEu+++y4YNGxg+fDhLly5lw4YNaLVaRo4cSUhICDt37sTe3p5F\nixYRHh7OokWLeO+995g3bx5hYWG0adOG6dOns3v3bnr16lVtz0AIIWrahAU7ajuESlk9s09thyDE\nTZWZNHr//ffL7LR161amTp1aLQEJIURVyS80cDIm7ZZL0xSTidRv15P2668kubTikmsAhSkK1nZm\nGP1T2W+2H0Wl0NTRnxH+Q/C087ijmPR6PcuWLaZ795507NgJnU5HRMRRzMwqv7xNCHF3GjFixE2P\n6/V6pk+fzvDhw8vs26lTJ9q0aQOAvb09+fn5REZGFs8MCg4OZvXq1TRq1IjWrVtjZ2cHQIcOHTh6\n9CgRERHF43ft2pWwsDCKiopISEgoHjc4OJiIiAhJGgkhhBCiTGW+pUjhVSFEXVfe0jRjbi6JK5Zz\nOS6TaN+R5Klt0KLCrlURR6y3UUQR7jYNGO4/hBZOTe64yDXAiRPHmDdvLj169Obbb38CkISREPe4\nH374gQULFpCZmQmAWq0mMDDwln00Gg3W1tYAbNiwgZ49exIeHo65uTkAzs7OpKSkkJqaipOTU3E/\nJyenUsfVajUqlYrU1FTs7f+a1fjnGOVxdLTGzKz6/i7U6eyqbezqJHGLipDnXXM2LhpW2yFUytDp\nPwJ192dF4q5ZtRF3mW8qoaGhABiNRo4dO0ZAQAAAO3bsoHfv3jUSnBBC3Il/Lk1bvmDXTVoFgBuo\nVODQWM3JeuFkqTKoZ27PaN9hdHHriFqlvqM48vLyMBj02NvXIyCgM0uXriQkZMAdjSmEqDs+//xz\nNm7cyPPPP8+KFSvYuHFj8cyg8mzbto0NGzawevVq+vfvX3xcUZSbtr+d42W1/af09LwKtauslJTs\nah2/utTFuHU6uzoZd10mz7tm1eWf8boYtzzvmlddcd8qGVXum9Brr73G7t27i78/ePAgr7zyStVE\nJoQQ1eTGrmlpuFdw17SUjicId/iZIrN8hvoOYE7QiwS5d7rjhFFcXCy9egUye/bLxcdGjRqLg0PV\n1EQSQtz97Ozs0Ol0GI1GrK2tGTNmDN9++225/fbu3cuHH37IRx99hJ2dHdbW1hQUFAA3dl10dXXF\n1dWV1NTU4j7Xrl0rPv7nLCK9Xo+iKOh0OjIyMorb/jmGEEIIIURZyn0bio2NZfr06cXfz5w5kytX\nrlRo8LfeeosxY8YwduxYTp48WeJcUlIS48aNY+TIkbz66qu3GbYQQtzaiZhU9AYTAU11FWp/TZNE\nT4+uzAl6iYE+fTHXmFdJHB4eDXF0dMTJybnCn+oLIe4tGo2GnTt34ubmxgcffMAvv/xCQkLCLftk\nZ2ezcOFCVqxYgYODA3CjNtGvv/4KwG+//UaPHj1o27Ytp06dIisri9zcXI4ePUpAQADdunVjy5Yt\nAOzcuZMuXbqg1Wrx9fXl8OHDJcYQQgghhChLuYU0CgoKyMjIKP6DJTk5mcLCwnIHPnjwIHFxcaxb\nt46YmBjCwsJYt25d8fkFCxYwYcIEQkJCmDt3LomJibi7u9/BrQghxF/+XJrW6Y+laVmpWbdsP6vL\ndOpbVyzBdCuKorBhwwbS0rJ46KFRmJmZsWnTNrRa7R2PLYSomxYuXMi1a9cICwvjvffe4+zZs8ye\nPfuWfTZv3kx6ejrTpk0rPrZgwQJmzZrFunXrcHd3Z/jw4Wi1WqZPn87EiRNRqVRMmTIFOzs7Bg8e\nzP79+xk3bhzm5uYsWLAAgLCwMF599VVMJhNt27YtscObEEIIIcQ/lZs0mjJlCg888ABubm4YjUau\nXbvGvHnzyh04IiKCfv36AeDn50dmZiY5OTnY2tpiMpk4cuQI7777LnBjCZwQQlSVgqK/lqa5u9hw\nNuIC+3bFgqrsxE1VJIwArl+/zsSJE7G0tGLIkAexsLCQhJEQ9zlnZ2e0Wi2xsbGMHj2aRo0aYWtr\ne8s+Y8aMYcyYMaWOr1mzptSxgQMHMnDgwBLHNBoN8+fPL9XW39+ftWvX3uYdCCGEEOJ+VW7SKDg4\nmG3bthEdHY1KpcLX1xcrK6tyB05NTaVly5bF3/+5m4etrS3Xr1/HxsaG+fPnc+bMGQICAkosgbsZ\n2bnj3iLPu+bdT898z7Er6A0murVswG9fHeLi5Tw0CnCLzc/u5PmYTCbS0tLQ6XTodHZ88cUXNG3a\nlIYNXSo9prg999PP991AnvftWbNmDR9++CE+Pj6YTCauXLlCaGgo//73v2s7NCGEEEKIW6rQPs+W\nlpa0atXqji7091oeiqKQnJzMo48+ioeHB5MmTWLXrl233JWtOnfuqMtV3+sied4173575jsOXsZR\nUUgIv4jeqMIhP4mM+scha1CZfSr7fAoKChg79iFyc3P55ZftmJmZMXToUFJSsu+rZ16b7ref79pW\nnc/7Xk1Gfffdd2zbtq14x7TMzEwefvhhSRoJIYQQ4q5XoaRRZdxsNw+d7sbyD0dHR9zd3fHy8gIg\nKCiICxcu3DJpJIQQt3L+yccB0KstaKALRP3/7N13eFRl+sbx78wkIb0XSAiQACH0jvQOUiyoqEhR\nVlwrAiuuIhZUVkFdXHVFd0UBxYYCCiICSpMqVSFA6BggJKRDkklmMjO/P1ii+UGYBFJIuD/X5bUz\n55z3PfeZVUieOc97fKKwWQuISdvBlnanIboOiTnLLjNDzys6r7u7OxERtTl37hw5Odn4+flf0Twi\nUn2FhYUVFowA/Pz8Cn8GEhEREbmWOS0aORwODIbL9HQUo0uXLvz73/9m2LBh7N27l9DQ0ML+fRcX\nFyIjIzl+/Dj16tVj7969DB48uPTpRUT+JMWzNvGhnbG4eOKbd4amyRvwtJ7F1uk++tTpjslYNi2u\nv/22i40bN/Doo48D8K9/vYurq+sV/VkpItXXggULAAgPD+fhhx+mc+fOGI1GtmzZQlhYWCWnExER\nEXHOadGcI/qXAAAgAElEQVTo3nvvZd68eaWeuE2bNjRt2pRhw4ZhMBiYMmUKixYtwsfHh379+jF5\n8mQmTZqEw+EgJiaG3r17X9EFiIjk5xWwL7QLp30bYnDYaJC6nTqZezFwvi22f71eZXYuu93OuHGP\nEh+/jwEDBhEdXR83N7cym19Eqo8dO3YUvg4ICGD//v0A+Pj4YDabKyuWiIiISIk5LRo1btyYt99+\nm9atWxd5AlCnTp2cTv7kk08WeR8bG1v4um7dunzxxRelySoicpETx9JZs+wAOb4N8clLpcmZDXhb\nMsv8POnpaQQGBmE0GvnXv/5NdnY20dH1y/w8IlJ9/P+nl2VmZmIwGPDz86ukRCIiIiKl47RodOFb\nse3btxduMxgMJSoaiYiUF6ulgM1rjrJ3VyIYHESl/Uq9jN0YcTgfXEqTJ/+db79dyPr12wgKCqJN\nm3Zlfg4Rqb527tzJU089RU5ODg6HA39/f9544w2aN29e2dFERERELstp0ehKWtNERMpTYkIma5bF\nczYzD4tnDglRu+iz+HC5nS8ysi5hYbXIyEgnKCio3M4jItXTjBkzeO+994iJiQFg3759vPLKK3z2\n2WeVnExERETk8ozODjhy5Aj33nsvbdq0oW3btowZM4aEhISKyCYiUkSB1cbGVYdZ/PmvZGWaSal1\nhOPNNjGgfpsyPU9S0mmmTXsZu90OwIMPPsLKlWtp0KBhmZ5HRK4PRqOxsGAE0KRJE0ymslmYX0RE\nRKQ8Ob3TaOrUqdx///106NABh8PBpk2bmDJlCnPmzKmIfCIiACQnnmXV0v1kpZuxuOdwIvo36tUJ\n5ZGYJ+C7lWQAPwW3J3zwQIZ0i76qc/3jHy/y1Vdf0KxZC26+eQgmk0m/4InIFTMajaxcuZLOnTsD\n8PPPP+vPFBEREakSnBaNHA4HPXv2LHzfr18/tayJSIWxFdjZtvE4u7YkgANSw46RG3WKYbGDaRvW\nioKMdI6vWYXZw4ddfjHcEht6RefJzMzA3z8AgOeff4kbbujE4MG3lOWliMh16qWXXmLq1Kk8++yz\nGI1GWrZsyUsvvVTZsUREREScclo0slqt7N27l6ZNmwKwe/dubDZbuQcTEUlNPsdP3+0jI9WMxS2X\nU9G7aRXbgCH1n8DT1ROAtCWLcRQUsDakJaHBPkQEe5X6PJ9++jHPPTeJxYuX0bJla8LCajJq1Ogy\nvhoRuV7l5uby0UcfVXYMERERkVJzWjSaNGkSEydOJD09HYCQkBCmT59e7sFE5Ppls9nZtTmB7RuP\n43BAekgCjtg0Hmx6Nw38owqPs5xO5OzG9diCQtntWY+bY0MxGAylPl+dOnXx9vYu/HNORKQsTZ8+\nnU8++aSyY4iIiIiUWrFFo3Xr1tGjRw/S0tJYvnw5586dw2Aw4O3tXZH5ROQ6k56aw8olcWScMWN1\nNZNUfx/dW7Wmb53huBiL/pGV+u0icDjYU68jjiwj7UrYmpabm8vMmW/z4IOP4OfnT/fuPdm69Tc8\nPT3L45JE5DoXHh7OqFGjaNmyJa6uroXbx48fX4mpRERERJwrtmg0bdo0jEYjb7/9Nh4eHjgcjiL7\nO3XqVO7hROT6Ybc7+PWXBLauP4bDDhlBJ/Fpmc+EZvcR6hl80fF5x46SvWM7bvWiWZ0dQK0gjxK3\npn366VzeeGMaFouFZ5+dAqCCkYiUm9q1a1O7du3KjiEiIiJSasUWje655x4++ugjTp06xcyZM4vs\nMxgMKhqJSJnJyshl2eLfyEzKp8Aln7SYQwzu2IV2Ya2KbTdLXbQQgPT2fbHsyqNdo8u3pp07dxZv\nbx8MBgOjRz9Afr6FMWMeLJfrERH5s7Fjx5KVlcXvv/8OQHR0tO7cFhERkSqh2KLRfffdx3333cdn\nn33GiBEjKjKTiFwnHA4HO7cdZ+u642AzkBV4mogObtzf9H68XIu/8ydn315y9+/Fs2kzfsz1AfJo\nf5nWtC1bNvHAA/fx3HMvMmzYCNzc3Hj88Qllf0EiIpcwd+5c3n//faKiorDb7SQkJDBu3DiGDx9e\n2dFERERELsvpQtgqGIlIeTibaea7xTs4e7qAApOV3CYnGNq9d5GFri/F4XCQumgBAL4338aeb09Q\nK8iTiJDiW9MiImpTUGAlO/tcmV6DiEhJfPPNN/z000/4+PgAkJWVxb333quikYiIiFzznBaNRETK\nksPhYPv2I2xbm4DBZuRcwBmadA9iQKP7Llro+lKyd+4g//gxvNu1J97qjaXAflFrmt1u57PPPqF9\n+xuIjW1MZGQdduzYi5dXydY8EhEpS8HBwYUFIwA/Pz+tcSQiIiJVgtPf0M6ePYuvr29FZBGRau5s\nVi7fLN5KbiLYTTYczc5wX68+hHmFlGi8w2Yj7ZuFYDQSPOR2vt18BuCi1rStW7cwceI4evbszVdf\nfQuggpGIVJrIyEgeffRRunTpgsPh4JdffsHf358FC87fNTl06NBKTigiIiJyaU6LRoMGDaJjx44M\nHTqUjh07VkQmEalmHA4Hm7bv49d1pzEWuGD2T6dDvzp0ie572cWr/7+zmzdiSTqNX/ceOAJD2X3k\nADUDz7emFRQUYLVa8fDwoGPHzrz88qvceuvt5XhVIiIlk5+fj5+fH3FxcQB4e3tjt9vZsWMHoKKR\niIiIXLucFo3WrFnDhg0bWLRoEa+//jr9+/fn9ttvJzS0+EVnRUQuSM86y6LFW7AmuuEwgnuLc4zs\n2x8ft9I9OchutZC25FsMrq4E3nQru46knm9Niw3l5MkTjB49gs6duzB16nQAHn54bHlcjohIqU2b\nNq2yI4iIiIhcEadFI1dXV3r16kWvXr04duwYzz77LO+//z79+vVj8uTJBAYGVkROEaliHA4Hq7bt\nIH59OiarGxa/c/Qc1IiWdWOuaL6sNWsoSE8n4MYBuAYGsn3dHgA6xIYS7OtCbm4O2dnZOByOUt29\nJCIiIteu+6evruwIIiLXNadFI7PZzIoVK1i0aBHZ2dncddddfPDBB6xfv55x48bx6aefVkROEalC\nEjPOsHjJL3DaB4PBREArO7f3G4CbyfWK5rOZzaQt+w6jhweBA28i32Jj16EUfN0dRIR4YTAY+PHH\ndXh7+zifTERERERERErEadGob9++9OzZkyeffJIWLVoUbh84cCA//PBDuYYTkarFZrex9Jf1JGzK\nx8Xqg93PzI03N6fBVT4lKGPlcuzZ2QQNuR2Ttzfrth7GZof47T9gsXSlRo0aKhiJyDXnP//5Dw8/\n/DDvvfcejz76aGXHEanSZk/qXdkRSi0kxIeUlHOVHUNE5Ko4LRrdc889jB1bdG2Qd955h3HjxvHO\nO++UWzARqVoOpRzn+2XbqXE6GJPBlfA2btzUpzsmk/Gq5i04e5aMlSsw+fji1rkLAPGnzAA8+dDd\n1KhR46qzi4iUhwULFpCTk8P333+P1Wq9aP/48eMrIZWIiIhIyRVbNNqyZQtbtmxhyZIl2Gy2wu1W\nq5VvvvmGcePGVUhAEbn2vD997SW31yAYo5+VQbe2IjI8pEzOlb7sOxz5eSzLzWH1mHv54svF7D6S\nRs1AT/p0bVcm5xARKQ9vvPEGmzdvBsBkMlVyGhEREZHSK7ZoFB0dTUpKClD0Bx0XFxfefPPN8k8m\nIlXSAw/2ueq7iy6wpqWStXYNLkHB7Dh9EpvNxpa4hMKnpmnBaxG5lrVu3ZrWrVtzww030LZt28qO\nIyIiIlJqxRaNQkNDufnmm2nTpg0REREVmUlEqrCyKhgdPXqY5DmzCSooIHjIbfy7WXO8vLz5z5J9\nALSPDS2T84iIlDd/f3/uvfde4uLiMBgMtGrVihdeeIG6detWdjQRERGRyyq2aDRhwgTeeusthg8f\nfslv89euXVueuUTkGpSVf5YlB1YCweV6HrvdztNj7uPl6AYYQsPwuaETBqORfIuN3UdSCQv0pHaI\nV7lmEBEpK1OnTuX++++nQ4cOOBwONm3axIsvvsicOXMqO5qIiIjIZRVbNHruuecA+PzzzyssjIhc\nmyw2K6tPrGfDrj2EHm2Eazmdx2w24+HhgdFo5PmefTCdSKDm3fdgMJ6/e2n30TQsVjvtY0PUmiYi\nVYbD4aBnz56F7/v168e8efMqL5CIiIhICRVbNHJ2J9HQoUPLOouIXGMcDgc7zvzGkn0/4nmoNhHp\nLTEYwVEO55o27WUWLvyaNWs24pKSgvuJBNzrN8C7ZavCY7bFnwGgfWxYOSQQESkfVquVvXv30rRp\nUwB2795d5CEjIiIiIteqYotGO3bsuOxAFY1EqrfjZxNYePA70o8UUCuhFSabK6Hh3vQe1JgvP9xW\n5uczGk2AgZMnTuD9/XcABN9xZ+EdRflWtaaJSNX09NNPM3HiRNLT0wEICQnhtddeq+RUIiIiIs4V\nWzSaNm1aReYQkWtERl4mi4/8wK8J8UQca0btsyG4uBrp2DuaZm0iMBgMPDKp51WfJzMzg6+//pIH\nHngYg8HAhAlPMnbsBDh+jFMH4vFs1gLPmEaFx+85otY0EamaWrZsyfLlyzl37hwGgwFvb+/KjiQi\nIiJSIk4Xwu7Ro4cWwha5DuQV5PNTwlp++v1nfE9HEHOyOwa7icjoQHrcGIOPn3uZnm/SpIksWrSA\n2rXrMHDgYGrUqIGbqysJC78GIPj2O4ocv/V/rWntGumpaSJSNfn4+FR2BBEREZFS0ULYItc5u8PO\n1qSdLDmynLxMO/V+v4Ea53yp4e5C174NaNg0rMzu7MnLy8Pd/XzxadKk52ncuCl9+/Yv3J+9czv5\nCb/j0+EG3Ov88Sjqwta0AA8iQ/UNvYiIiIiISEUotmgUHHz+kdoBAQF88803HD58GIPBQExMDLfe\nemuFBRSR8nM48xgLDy3hRFYiYUkNiUysD3YDDRqH0qVvAzy93MrsXIsWfc3zzz/Dt98uo2HDGOrV\ni2L8+ImF+x0FBaR+sxBMJoJuvb3I2MLWtMahak0TERERERGpIMUWjS4YN24cgYGBtG7dGofDwfbt\n21m7di3/+c9/KiKfiJSDVHMa3x5exq6UPXhk+9HsRF8c51zx8nGje/8Y6jUMLvNz1qjhTl5eHkeO\nHKZhw5iL9mdt2oA1ORm/Hr1wCyv6dLRtak0TkSrs0KFDfP3112RlZeFw/PH8yddff70SU4mIiIg4\n57RolJ2dzYcfflj4fvjw4YwYMaJcQ4lI+TAX5LHi+GrWnFiPrQAanemA64lgHECT1uF07BFNDXen\nfyyUSEFBAR9/PJt77hmJp6cngwffTKdOnQkMDLroWLvFQvp3izG4uRF08y1F9uVbbfym1jQRqcIm\nTJjAwIEDady4cWVHERERESkVp78d1qtXjzNnzhAaev4b/pSUFOrWretklIhcS+x2OxtObeG7oyvI\ntuZQM7cu4cebYcl24BfgQc+BjQiv41+m55w16z9MmTKZlJQzTJp0fo20SxWMADLXrKIgI4OAAYNw\n8Q8osu9Ca1q7WLWmiUjVFBwczNixYys7hoiIiEipFVs0Gj58OAaDgfz8fPr160d0dDQGg4GjR4/S\ntGnTiswoIlchPv0Qi3csIyHrFO52TzqmDSD7mBGrwUHrjnVo16UuLq6mMjmXxWLBze38Okj33Xc/\nZ84k89BDj152jC03h/Tvl2L09CRw4OCL9l9oTWsfq9Y0EamaunfvzoYNG+jQoQMuLn/86GU0Gp2O\nPXjwII8++iijR49m5MiRTJo0ib179+Lvf77QP2bMGHr27MmSJUv4+OOPMRqN3HXXXdx5551YrVYm\nTZpEYmIiJpOJadOmERkZSXx8PC+++CIAjRo14qWXXiqX6xYREZGqr9ii0YQJE4odpG/7Ra59yTln\nWHT4e+LS9mNwGGhHF9gXRHZuAcFh3vQc2IiQmmX3+Oddu3bw8MNjmDLlHwwadBOenp5MmTLV6biM\nFcux5+YQfPtQTF5eRfapNU1EqoP333+f7OzsItsMBgP79++/7Ljc3FymTp1Kp06dimx/4okn6NWr\nV5HjZs6cyYIFC3B1dWXo0KH069ePNWvW4Ovry4wZM9iwYQMzZszgrbfe4pVXXmHy5Mm0aNGCiRMn\nsm7dOnr06FF2FywiIiLVRrFFow4dOhS+zsnJISsrCzh/J8GTTz7JggULyj+diJRajjWXH479xLpT\nm7A77DR0j6FBUhtOHDyLyWSjY89oWrSvjcnk/Bvu0vDy8iYp6TSHDh0AbirRmIKsLDJ+XIHJzw//\nPv0u2q/WNBGpDrZv335F49zc3Jg1axazZs267HG//fYbzZs3x8fn/BcBbdq0YefOnWzevJkhQ4YA\n0LlzZyZPnozFYuHUqVO0aNECgF69erF582YVjUREROSSnK5pNGvWLP773/9isVjw9PQkPz+fm2++\nuSKyiUgp2Ow2fj61mWXHfiS3wExwjUA62/tw8pc8TuSfpVakHz0HNsI/0LPMzrl06RKaN29B3br1\niIlpxPbtcYSEhJR4fPr3S3BYLATdOQxjjRoX7d9+QK1pIlL15eTkMHfuXPbs2YPBYKB169bce++9\nuLu7X3aci4tLkXa2Cz799FPmzJlDUFAQzz//PKmpqQQGBhbuDwwMJCUlpch2o9GIwWAgNTUVX1/f\nwmODgoJISUkpoysVERGR6sZp0WjFihVs2rSJMWPGMG/ePFatWkViYmJFZBOREnA4HOxNi2fR4aUk\n56bgbnJncNggLL/5cTQhC1c3E4PuaE6dBoFlerfOxo3ruf/+kfTvP4BPP/0KoEQFo4MPjL5o25nP\nPuHMZ58Q8+Hcwm35Vhu/Hk4lVK1pIlLFPf/884SFhTFs2DAcDgebNm3iueee45///Gep57r11lvx\n9/encePGfPDBB7z77ru0bt26yDEOh+OSYy+1vbhj/ywgwBMXl7JZ++5SQkLKrlW6Iil3xVJuKamq\n+pkrd8VS7pJzWjTy8vLCzc0Nq9UKQJ8+fRg9ejSjRo0q93AicnmJ2UksPPQd8RmHMGCga61ORGc0\nZ/cPiRQUZFG3fhDdb2xIVP0QUlLOXfX5HA4HNpsNFxcXOnfuyoQJT3LXXfc4H2e3Y8/Px242l/hc\nF1rT2qs1TUSquNTUVN58883C97169brin6P+vL5R7969efHFF7nxxhtJTU0t3H7mzBlatWpFaGgo\nKSkpxMbGYrVacTgchISEkJmZWXhscnJy4RNyi5ORkXtFWUuqLP5+qgxVMXdIiE+VzA36vKVkqvJn\nXhVz6/OueOWV+3LFKKdFIz8/P5YsWUJMTAzPPPMM9evX58yZM2UaUERK55wlm6VHV7AxcSsOHDQO\njKGPf1/2rk1jZ9JJ3D1d6TmoEQ0aX13BxeFw4LBYsJvNJCf8zvSXX6BN02YMveU27GYzj3ToiH3/\nPlJ27sCeZ8aWa8aeZ8Zu/tM/eWbseXlQgm+z/0ytaSJSXZjNZsxmMx4eHsD5havz8/OvaK7HH3+c\np556isjISH755RcaNmxIy5Ytee655zh79iwmk4mdO3cyefJksrOzWb58Od26dWPNmjXccMMNuLq6\nEh0dzfbt22nXrh0rV67UF4EiIiJSLKdFo9dee420tDT69evHxx9/TFJSUpFvy0SkdC7VnnXBn9uz\nLsVqL2DtiQ0sP76aPFseYZ6hDIkahDnenZ9X/o7d7iCmaRid+9SnhqsBW/Y57OY8ss+mkJuYWljE\nsZkvUdj50+vC/Xl5YLMVnv9x/wA4dYrT7797+Ys0GjG6e2D09MAlKBiThwdGDw+M7h6c27rF6WeU\nb7Xx2+E0taaJSLVw9913M3DgQJo1a4bD4WDfvn2MHz/e6bi4uDhee+01Tp06hYuLCytWrGDkyJFM\nmDABDw8PPD09mTZtGu7u7kycOJExY8ZgMBh47LHH8PHxYdCgQWzatIl77rkHNzc3pk+fDsDkyZN5\n4YUXsNvttGzZks6dO5f3RyAiIiJVlNOikYeHB3l5eaxdu5Z69erRv39/oqOjKyKbyHXJYbNhz8vD\nbs7Fbs7DZs7FbjZzNPkgu0/uwJKbzQ02Fxp4ROBqC+XXdQlkO9xxd+TR1LyHoNUJnPzejKOgoPQn\nNxgwurtj9PDAxc+fPF87BSYXAmvWwuTpgdluxzswCJOn5/mi0J+KQSZPj8JtBje3Yu9wKknRaM+R\nNPKtNrWmiUi1MHToULp06cLevXsxGAy88MILhIWFOR3XrFkz5s2bd9H2G2+88aJtAwYMYMCAAUW2\nmUwmpk2bdtGxDRo04PPPPy/FFYiIiMj1ymnRaPr06axatarw27EZM2YwcOBAnnjiiYrIJ3JdOfTo\ngzgslkvucwc6/O91gcGFI0E+nPSLBKB21n4aZMfh5u6Kycsb1+CQIgUdr0A/8jGdv+Pnz8WePx1j\n9PDAWKMGBqMRgJSUFLq3bUpoaBibNu3Azc2tAj6B8y60prVrpNY0Eam61q1bR48ePViwYEGR7evX\nrwfOF5NERERErmVOi0Zbt25l2bJluLq6AmCxWLj77rtVNBK5As6eUuNWK7ywkGNzM3E8/wwJ1lTy\nXSEkIJzWddqTmx/I9r1mcsw2/Pxr0KNvFOFR3TGYin+yTWkWqSsoKMDFxYWQkBCefXYKjRo1rtCC\nkeVPrWl1wtSaJiJV14EDB+jRowc7duy45H4VjURERORa57RoFBoaiulPv4y6uLgQGRlZrqFEqiNr\nSgpJH8++7DF1n38Ri83CqoSfWZmwFovNQoR3A25vcBN13euyadURDu5Nxmg00LZzXdp0rlNmj0HO\ny8vj2WefIiUlhY8//hyDwcBDDz1WJnP/mbN1m/YcVWuaiFQPDz74IABdu3Zl8ODBRfZ98cUXlRFJ\nREREpFSKLRq9/fbbAHh5eTF06FDat2+P0Whk69atNGzYsMICilR1DrudzLWrSV34NQ4nT8t5bPVT\nha99XL0Z2uBmOtZqx9H4VL78aRt5uVZCavrQa1Ajgsp4gWg3NzeOHDlMZmYmWVmZ+PsHlOn8JbUt\nXq1pIlI97N+/n7i4OGbPno3ZbC7cXlBQwMyZM7nnnnsqMZ2IiIiIc8UWjS7cXRQVFUVUVFTh9l69\nepV/KpFqwpKcTPLHszEfPIDRy4uwUfeR9OEHJRo7pdNT2HJhxcJ9/H4kDRcXI5161adF+wiM/1t3\n6GolJyexa9dOBgwYhNFoZNasj/H39y9sR61oha1p/mpNE5Gqz83NjbS0NM6dO1ekRc1gMPDUU09d\nZqSIiIjItaHYotHYsWMLX+fm5nLs2DEMBgNRUVF4eHhUSDiRqspht5P504+kfrsQh8WCd5u2hI4Y\nhYufP74d/3i08Z/vLCo6ARzdk8bmNUexWmyE1/Gn58BG+AWU3X97drud224bzMmTJ9iwYRt16tQl\nJCSkzOa/Ehda09qpNU1EqoH69etTv359OnbsSKtWrSo7joiIiEipOV3T6KeffuLFF1+kZs2a2O12\nUlNTmTp1Kj169KiIfCJVjuV0IklzZ5N35DAmbx9C//IA3u3aX1QEybHmXnK8m9mLiOPN+PncIdxq\nmOg5sBGxLWqWWRHFZrNhMpkwGo1MnjyF1NQUate+NtYpu9Ca1j5WrWkiUn28/vrrl/wz/LPPPquE\nNCIiIiIl57Ro9OGHH7JkyRICAwMBSE5OZvz48SUqGr366qv89ttvGAwGJk+eTIsWLS46ZsaMGfz6\n66/MmzfvCuKLXDscNhsZK1eQtngRjoICfNp3IGT4SFx8fC869uS5RL6beZBmDCp2vqiGwXTr3xAv\nnxpllvG99/7NggXzWbbsJ9zd3bnpplvKbO6rpdY0EamuJkyYUPjaarWyZcsWPD09KzGRiIiISMk4\nLRq5uroWFowAwsLCSrTeydatW/n999+ZP38+R44cYfLkycyfP7/IMYcPH2bbtm2Vtn6KSFnJP3WK\npDkfkn/8GCZfX0JH3odPm7aXPHZ78q98uv9rGtGv2PkSGuzk4dv/VuYtWomJJ0lKOs3hw4do1qx5\nmc59tdSaJiLVVYcOHYq879KlC3/9618rKY2IiIhIyTktGnl5eTF79mw6dz6/DsuGDRvw8vJyOvHm\nzZvp27cvcL6nPysri+zsbLy9/7iDYPr06fztb3/j3XffvdL8IpXKUVBA+vJlpC9dcv7uoo6dCB02\nApP3xXfK2Ow2Fh/5gVUnfsbddPm7h84GJpVJ4cRsNvPDD0t56KH7AZg06XmeeOIpAgODrnrusqbW\nNBGprk6cOFHk/enTpzl27FglpREREREpOadFo1deeYW3336bJUuWYDAYaNWqFa+++qrTiVNTU2na\ntGnh+8DAQFJSUgqLRosWLaJDhw5ERESUKGhAgCcuLqYSHXslQkJ8ym1uuVh1+Lxzjh/n0NvvknP0\nGG6BgdR/5EECO7S/5LFn87N5a9Nc4s4cINwnjCe7PsTszduLnfuru98vk4zDhz/EF198Qb16Edx4\n443X7Oeeb7Wx+0gatYK8aNusVrW40+ha/ayrK33eFUufd+ncd999ha8NBgPe3t5FHjgiIiIicq1y\nWjSKi4vj5ZdfvuoTORyOwteZmZksWrSIOXPmkJycXKLxGRmXXjS4LISE+JCScq7c5peiqvrn7Sgo\nIO3770hfthRsNny7dCPk7mHYPL0ueV0J504ya8880vMyaB7chPuaDMOaevmiyNV8Pna7HaPRCMDD\nD4/Hzy+Irl27XtOf+Y4DKeRZbLRuGExqanZlx7lqVf3f8apGn3fFKs/Pu7oWo1avXl3ZEURERESu\niNOi0dy5c+nSpQsuLk4PLSI0NJTU1NTC92fOnCl8nPeWLVtIT09nxIgRWCwWEhISePXVV5k8eXIp\n44tUrLzfj5M05yMsJ0/gEhBI2H2j8Wp28QLvF/xyegdfHFhIgd3GTVH9ubFeb1KTsvlhYVy55Fu5\n8gemTHmWr79eTO3akTRu3ISXX34VLy8vcnOv3V+qtx9Qa5qIVF+HDx/m3//+N4cPH8ZgMBATE8PY\nsThq37AAACAASURBVGOJjo6u7GgiIiIil+W0EuTj48PgwYNp0qRJkQWrX3/99cuO69KlC//+978Z\nNmwYe/fuJTQ0tLA1bcCAAQwYMACAkydP8swzz6hgJNc0u9VK+tIlpP/wPdjt+HXvQfDQuzEV8/Qb\nm93GosNLWXtyI+4md8a0GEnz4CYc3n+G1d/HYyuwl0vOtLQ0TpxIYOfO7dSuHVku5yhrFquNXw+l\nEuLvrqemiUi19NRTTzF8+HDGjRsHwI4dO/j73//OwoULKzmZiIiIyOU5LRr16tWLXr16lXriNm3a\n0LRpU4YNG4bBYGDKlCksWrQIHx8f+vUr/qlRItca89GjJM/9CEviKVyCggi77368mjQt9vhzlmw+\njJvH4cxj1PQK48Hm9xLqEczWn4+xY9PvuLqZuPHO5tStf/WLUTscDhYvXsTgwbfg6urKsGEj6Ny5\nK3Xr1rvquSvKnqPp/3tqWkS1WMtIROT/8/LyYujQoYXv69evz4oVKyoxkYiIiEjJOC0a3XbbbRw8\neLDwlupGjRqV+HbqJ598ssj72NjYi46pXbs28+bNK2FckYpjt1hIW/wNGSuXg8OBX6/ehNxxJ0Z3\nj2LH/H72BB/s+YTM/CxahTRnVOM7MdldWfntXo4eSMXX352BdzQnMMT5EwhL4v333+XFF5/luede\nZNy4JzAYDFWqYAR/tKZ1iA2r5CQiImXLbj9/V2mnTp1YuXIlnTt3xmAwsHnzZtq3v/SDE0RERESu\nJU6LRq+99hqrVq2iefPm2O12ZsyYwU033cSECRMqIp9IpTAfPkTS3I+wJiXhGhJC2H334xnb+LJj\nNidu48uD32Cz27glegD96/Yi51w+3y3cRWpyNuGRfvS/rSkenm5Xle3PC12PGDGK+Ph93HHHXVc1\nZ2WxWG38elitaSJSPTVp0gSDwVDkYSAXuLi48PDDD1dCKhEREZGSc1o0+uWXX/j+++8L1zOyWCwM\nGzZMRSOpluz5+aR+u4jMn1YC4N+3H8G3DcVYo0axYwrsBSw89B0/n9qMh4sHDza/j6ZBjUg6lcXy\nRXGYc6w0blmLbv0bYjIZryrfvn17GT/+UV544WW6deuBn58/77zz/lXNWZnijqWTb7HRro1a00Sk\n+omPj6/sCCIiIiJXxWnRKDg4uMiT01xdXYmIiCjXUCKVIffgAZLnzsZ6JhnXsDBqjn4Aj4YNLzsm\nK/8cH8bN42jWccK9avJg8/sI8QziYFwSa384gN3uoEvfBjRvWzZFEYsln7i43Wzc+DPduvW46vkq\n27Z4PTVNRKqvhQsXcscdd/D2229fcv/48eMrOJGIiIhI6TgtGgUEBHDHHXfQsWNHHA4H27ZtIzIy\nsvAHIP3AI1WdPS+P1EVfk7l6FRgMBNw4gKBbb8fodvk2sqNZv/Phnk/IspyjbWhLRjS+EzejK1vW\nHmXXlgTcapgYOKQpkVGBV5Vv06YN1K/fkLCwMFq1asPGjduJjq5/VXNeCy60pgX7uVM3zKey44iI\nlLkLrcQmk6mSk4iIiIhcGadFo8jISCIj/3h0d8+ePcszj0iFyt2/j+SP52BNTcGtVjhhfxmDRwkK\nMhtObeGrg4uxO+zc1mAwfSK7Y7XYWL40juOH0vAL8GDg0GYEBF3dgtcbN67nttsGc+uttzNr1lyA\nalEwgj9a03qrNU1EqqnbbrsNgFq1anHHHXdUchoRERGR0nNaNBo7dmxF5BCpUDazmdQF88latxaM\nRgIH3UTgzbdgdL383UVWewFfH/yWjYlb8XL15P6mI4gNbMi5rDyWLdhDekoOEXX96T+kKe4erlec\nz+FwYDAY6NSpC6NGjWbEiHuveK5r1Xa1ponIdeLHH3+kf//++PjorkoRERGpWpwWjUSquoMPjC52\nn1tEbWr+ZQzu9aKczpOZn8WsPfM4fjaB2t7hPNj8XoI8Ajl98vyC13m5Vpq2CadLnwZXvOB1SkoK\nTz/9BB063MDDD4/FaDQyY8Y7VzTXtcxitbFLrWkicp3Iy8ujd+/eREVFFT5YBOCzzz6rxFQiIiIi\nzqloJNe1us+/iMHF+X8GhzOP8WHcPM5Zsmkf1prhsXfgZnIjfvdp1i0/iMPhoFv/hjRrc3WLxBsM\nBjZtWk9OTjYPPfRYtW3bKmxNa63WNBGp/h599NHKjiAiIiJyRUpUNMrIyODkyZM0b94cu91euLCj\nSFXnrGDkcDj4+dRmFhxaAsDQhrfQs3YXHA7YtPoIv209QQ13F/oPaUrtegFXlOHo0SPk5ubSrFlz\ngoODWbbsJ+rVi67WxZQLrWnt1JomIteBRYsWMX369CLbxowZQ4cOHSopkYiIiEjJOC0aLV26lHfe\neQc3NzeWLl3K1KlTadKkCXfeeWdF5BOpNFablS8PfMOWpO14u3oxptlIYgLqY8kv4Kcl+/j9SDr+\ngR4MHNoc/0DPKzrHmTNn6N27C5GRdVi9eiOurq5ERzco4yu5tlgL/nhqWr2aak0TkepryZIlfPnl\nlxw6dIgRI0YUbrdaraSlpVViMhEREZGScVo0mjNnDosXL+bBBx8E4Omnn2bUqFEqGkmVcHbLpisa\nl5GXyQd7PiHh3Enq+ETw1+b3EugewNlMM8sW7CEjNZfIqAD63dqEGu6lX/D6wkLXoaGhPPzwY8TG\nNsGlBG1y1UHc0XTyLDZ6qTVNRKq5W265hRtuuIEnn3ySxx9/vHC70WikQYPq/QWBiIiIVA9Of0v1\n8fHBw8Oj8L27u3uRRRxFrlXpK34g9ev5pR53MOMIH8V9SrY1h44123F3o9twM7mSmJDJim/iyDMX\n0LxtBJ371C91q2Z+fj5vvDGN1NQU3nprJgCTJj1f6oxV2Ta1ponIdSQsLIyPPvqI9PR0atasSXx8\nPPHx8TRt2rSyo4mIiIg45bRoFBAQwDfffEN+fj579+5l2bJlBAYGVkQ2kSvisNtJ+Xo+mT+uwCUg\ngIKMjJKNczhYe3Ijiw4vBeDumCF0i+iEwWBg36+JrF95CIAeA2Jo0ir8irKZTCbWrVtDRkY6WVmZ\n+Pn5X9E8VZVa00TkevTMM8/Qr18/WrduzeOPP06/fv1Ys2YNb7/9dmVHExEREbksp0Wjl156ibfe\neoucnByee+452rZtyz/+8Y+KyCZSao6CApJmf8i5rVtwqxVOxN8m4hoY5HScxWbh8/iFbEvehY+b\nNw80G0UD/yjsdjsbVx1mz/ZT1HB34cbbmhJRt3QLXmdlZbJ3bxydO3fFxcWFjz76hKCgYLy8vK70\nMqsstaaJyPUoOTmZAQMGMGfOHIYPH85f/vIXRo8eXdmxRERERJxyWjTy9fXlhRdeqIgsIlfFZjZz\n+r13yd2/F/cGDYkYOx6Tt7fTcWnmdD7Y8wknsxOp51uHvzYfhX8NP/LzrPy4eB8njmUQEOzJoKHN\n8fX3cDpfkUw2G4MH9+P06dNs3LiNmjVrUadO3Su9xCpv2wG1ponI9cdiseBwOPjxxx955ZVXAMjN\nza3kVCIiIiLOOS0a9ejR45J3BKxdu7Y88ohckYKsTE69/S/yE37Hq1Vraj34CEY3N6fj4tMPMXvv\nZ+RYc+kS3oE7Y4bganQhMz2XHxbsITPdTJ36gfS7pQluNUq+UPWFha5NJhOPPTaepKTTBJbgjqfq\nzFpg49dDak0TketPhw4daNu2Ld26dSMqKoq5c+cSFRVV2bFEREREnHL6W/Dnn39e+NpqtbJ582by\n8/PLNZRIaViSkzj1rxlYU1Pw696T0BGjMJhMlx3jcDhYdeJnvj28DKPByD2NbqdrREcATh7PYOW3\ne8nPK6Blh9p07Fkfo7HkrVSffz6PhQu/Zv78Rbi4uHDPPSOv6vqqiwutaT3VmiYi15knn3ySBx98\nEF9fXwD69u3LyJH6u0FERESufU6LRhEREUXe16tXjzFjxqgXX64JeceOcurtf2HLPkfQLUMIvPlW\npwWJfJuFz/Z/zY4zv+Hn5sMDze8l2u98y1jczlNs+PEQBoOBXoMaEduiVqkzbd68kV27drB//16a\nN295RddVHV1oTWuv1jQRuU7897//5aGHHuLvf//7Jf9uev311yshlYiIiEjJOS0abd68ucj7pKQk\nEhISyi2QSEnlxO0m8f2ZOCwWQkeNxr9HT6djUnLT+GDPxyTmJBHtV48Hmo3Er4YvNpudTasOE7cz\nEXcPVwbc3pRakSV7sllBQQHr16+jV68+ALz88qtMmvQcERG1r+byqhW1ponI9ahJkyYAdO7cuZKT\niIiIiFwZp0Wj9957r/C1wWDA29ubl156qVxDiThzdvNGkubOxmA0Ev7oWLxbt3U6Zm/aAebs/Rxz\ngZnuEZ24o+HNuBhdyM+zsuKbvZz6PZPAEC8G3tGsVAtejxv3CAsWzGfJkuV07NiZgIBAAgICr+by\nqp24Y2pNE5HrT7du3QBo0aIFBw8exGQy0aRJE2rX1pcKIiIiUjU4LRpNmjSJpk2bVkQWEaccDgcZ\ny38gdeFXGD09iXh8Ah4NY5yOWfn7Gr47ugKTwciI2DvpHN4egIy08wteZ2WYqdcgiD43Ny7VgtcA\n99//V1xcXIiJaXTF11XdbYtXa5qIXH/y8vKYOHEi8fHxNG3alOzsbPbv30/Xrl155ZVXcCvBAxtE\nREREKpPR2QGvvfZaReQQccpht5My/3NSF36FS0AgkU8/67RglFeQx4dxn7Lk6HL8avjyt7aPFBaM\nThxLZ9EnO8jKMNO6Yx0G3NGsRAWjzZs3MmhQX1JSUgBo164D77zz/nX/dLTiqDVNRK5XM2fOJCws\njBUrVvDOO+8we/ZsVq9eTY0aNfjXv/5V2fFEREREnHL6G3J4eDijRo2iZcuWuLq6Fm4fP358uQYT\n+TO71Ury7Fmc27YVt/AIIiZMxDXw8i1gZ3JT+O+eT0jKSaaBfxQPNBuFj5s3DoeDPTtOsWnVYQxG\nA71viqVRs5olzrJ796/s3LmddetWM3To3Vd7adVeYWtaK7Wmicj1ZceOHcydOxcXlz9+3PLw8GDK\nlCncfvvtPP3005WYTq7WwQdGV3aEUjsIxHw4t7JjiIhIFeK0aFS7dm313kulspnNJM58B3P8fjwa\nxhA+djwmL6/LjtmTuo+P932JuSCPXrW7cluDwZiMJmw2Oxt+PMS+X0/j4eXKgNubUTPCz2mGn39e\nS9eu3TEajTzwwMN07tyN5s1blNUlVmvbL7SmNVZrmohcX0wm0yVb0FxdXfH19a2ERCIiIiKlU2zR\naMmSJdxyyy2MHTu2IvOIFFGQmcmpt2eQf+IE3q3bUvOvD2H8fz+AP7b6qWLH39v4bm6odX6R7Dzz\n+QWvExMyCQ71ZsAdzfDxc3ea4YMP3uO55yYxbdobjBnzECaTSQWjErIW2Pj1sFrTROT6dLm7K00m\nUwUmkfJQ1e7YqYp3RomISOUrtmi0YMECbrnllorMIlKEJSmJk2/9k4LUVPx69CJ0xCgMRqfLcBVx\noWCUnprDDwv2cDYzj6iYYPrc1BhXt5L9wD5kyFDWrVtDt249S3sJ1724Y+mY8230aKnWNBG5/uza\ntYuePXtetN3hcJCRkVGiOQ4ePMijjz7K6NGjGTlyJKdPn+app57CZrMREhLCG2+8gZubG0uWLOHj\njz/GaDRy1113ceedd2K1Wpk0aRKJiYmYTCamTZtGZGQk8fHxvPjiiwA0atRIT8UVERGRYpXuMVEi\nFcR89AiJ77yFLfscQUNuJ3DwzVdcdPj9SBo/Lt6H1WKjbee6tO9W77JzHT9+jKee+hvPPPM8rVu3\nJTQ0lM8++/pKL+W6dqE1rZ2emiYi16Hly5df1fjc3FymTp1Kp06dCre98847DB8+nIEDB/Lmm2+y\nYMEChgwZwsyZM1mwYAGurq4MHTqUfv36sWbNGnx9fZkxYwYbNmxgxowZvPXWW7zyyitMnjyZFi1a\nMHHiRNatW0ePHj2u9nJFRESkGiq2aHS5b8cMBgNr164tx1hyPcve/Run/zMTh9VK2L1/wa978T/I\nWu0FxU/kgN+2nmDzmiMYTUb63tKYhk3CnJ7/5MkTrF27mtjYJrRu3fZKLkEAa4GdXw+nEuTrTlQt\ntaaJyPUnIiLiqsa7ubkxa9YsZs2aVbjtl19+KbwzqFevXsyePZuoqCiaN2+Oj8/5P2vbtGnDzp07\n2bx5M0OGDAGgc+fOTJ48GYvFwqlTp2jRokXhHJs3b1bRSERERC6p2KJRkyZNePPNNysyiwhZG9eT\n/PEcDCYT4Y+Nw7tV62KPPX42gXn7L30HkMFuJPx4UzalHsHT240BtzcjLLz4RUfj4vZQu3Zt/P0D\n6Nq1O8uXr1bB6CrtVWuaiMhVcXFxKfLkNQCz2Vy4uHZQUBApKSmkpqYS+KcnigYGBl603Wg0YjAY\nSE1NLbII94U5RERERC6l2KKRm5vbVX9DJlJSDoeDjB++J3XRAoxeXkQ8PgGPBg0veazVZuX7Yz/y\nU8I6HDgu2m+yulHnUBu8sgMJqenNgDua4+1To9hzb9y4nqFDb2HYsBH861/vAtCmTbuyubDr2Lb4\nZECtaSIi5cXhuPjvwNJuL+7YPwsI8MTFpfwW7g4JqZp3o1a13Af/979VLfcFyi0lVVU/c+WuWMpd\ncsUWjS7ctixS3hx2Oylffk7m6p9wCQwkYsKT1AgPv+Sxx7J+Z97+r0nOPUOQeyC1fu5Y7LxZgad5\nYMRduLpe/gfdDh060q/fAG6+echVXYf8Qa1pIiLlw9PTk7y8PNzd3UlOTiY0NJTQ0FBSU1MLjzlz\n5gytWrUiNDSUlJQUYmNjsVqtOBwOQkJCyMzMLDz2whyXk5GRW27XA5CScq5c5y8vyl2xqmLukBCf\nKpm7KqvKn3lVzK3Pu+KVV+7LFaOKfRTV3//+93IJI/JndquF0x+8T+bqn3CLqE3kM89fsmBksVlZ\ndHgpM3a8R3LuGXrU7sKzNzxx2bmf/uuwSxaMzp7N4sknJ/Dll58B4OrqyieffEHv3n3L5qKksDWt\nXWyIWtNERMpQ586dWbFiBQArV66kW7dutGzZkj179nD27FlycnLYuXMn7dq1o0uXLoWLca9Zs4Yb\nbrgBV1dXoqOj2b59e5E5RERERC5FT0+TSmPLzSVx5juYD8TjEdOI8LHjMHl6XXTckczjfBr/FWdy\nUwn2CGJk7J00DIh2On9xxYpz586xcOFXHDp0gLvvHq6iRjnY9r+nprWPdb7wuIiIXFpcXByvvfYa\np06dwsXFhRUrVvDPf/6TSZMmMX/+fMLDwxkyZAiurq5MnDiRMWPGYDAYeOyxx/Dx8WHQoEFs2rSJ\ne+65Bzc3N6ZPnw7A5MmTeeGFF7Db7bRs2ZLOnTtX8pWKiIjItUpFI6kUBZkZnHzrTSwnT+Ddth01\nH3gQo6tbkWMsNgtLji5n7YmNAPSK7Mot0QNwM7ldasrLSk5OJicnm+jo+kRE1GbRou9o2rS5Ckbl\n4HxrWopa00RErlKzZs2YN2/eRdvnzJlz0bYBAwYwYMCAIttMJhPTpk276NgGDRrw+eefl11QERER\nqbZUNJIKl3vyFAnT/kFBWhp+vXoTes9IDMainZKHM4/x6f6vSDGnEeoRzMjGd1Hfv17hfpvNzq4t\nCSU6X3JyEl27dqB+/fp8//1PmEwmPRmtHF1oTeveMlxFORERERGRErh/+urKjnBFZk/qXdkRpJyp\naCQVynzkMEfffZuCc+cIGnI7gYNvLlJYyLdZWHLkB9ad3ARAn8ju3BTdv8jdRempOaxeup+UpOwS\nnTMsrCa33no7jRs3URGjAlxoTdNT00RERERERKo2FY2kwmTv/pXT/3kPR0EBYaPvx69r9yL7D2Uc\n4dP9X5Oal06YZwgjG99FtF/dwv12u4Pftp1g28/HsNkcNGoWxoG45GLPN3XqFJ5//iUA/vnPt8rn\noqSIP1rTahBdy7ey44iIiIiIXNOq6p06VfXOKCk9FY2kQmRt+JnkT+ZicHGh8bOTsNWNKdyXV5DP\n4iM/8POpTRgw0K9OTwZF9cPN5Fp4TGZ6Lmu+jyfp1Fk8PF3pMaARUTHB9L6p8UXnysvLo3fvLmRl\nZTF+/BP4+vpVyDUK7D2u1jQREREREZHqQkUjKVcOh4P0778j7dtFGL28iBj3NwLbtSYl5RwAB9IP\n81n816TlZVDTM5SRje8iyq9OkfFxO0+xZc1RCgrs1I8NoVv/hnh4Fl0M22w2c/jwIZo3b4G7uzuz\nZ39KaGioCkYVbLta00RERERERKoNFY2k3Djsds588SlZa1bjEhRE7QkTcasVDkBeQR7fHFnGhlNb\nMBqM9K/bi0H1+uL6p7uLzmaaWbPsAIkJmdRwd6HX4FgaNL64GGGz2Rg0qC/JyafZsGEbgYFBxMZe\nfAeSlC9rgZ1dh9SaJiIiIiIiUl2oaCTlwm61kPThB2Tv2I5b7UhqT3gCF/8AAHYn7WfmL5+QkZ9J\nuFdNRja+k7q+kYVjHQ4H+3efZtOqI1gtNuo1CKLHgBg8vWtc8lwmk4m77rqHxMSTuLld+hgpfxda\n07q1UGuaiIiIiIhIdaCikZQ5W24Oie++g/ngATwaxRL+2DhMnp6YC8x8c/h7NiZuxWgwMqBeHwbU\n64Or8Y9/DbPP5bPuhwMkHE3HrYaJXoNjadQs7KIixA8/fM+33y7g/fc/wmg08sgjYyv6MuX/udCa\n1l6taSIiIiIiItWCikZSpqwZGZx6awaWUyfxbteemmMexOjqyt60A3wev4DM/Czq+kUwLOYO6vjU\nLhzncDg4tDeZ9T8expJfQGRUAD0HNsLb1/2S55k//3N++mkFcXG7adGiVUVdnhTjfGta6vnWtHC1\npomIiIiIiFQHKhpJmclPTOTUW/+kID0d/959CBk2ArMtn0X7v2Xz6W0YDUYG1evLyHa3kpFuLhyX\nm2Ph5xUHOXYwFRdXI91vjKFJq1pF7i5yOBzs3v0rLVu2BuC112aQmfk8jRrFVvh1ysXOt6YV0K1F\nLbWmiYiIiIiIVBMqGkmZMB8+xKl33sKem0Pw7UMJGDiYvWnxfHFgEZn5WdT2Dmdk47uI9AnHxfTH\nv3ZH4s/w84pD5JmthEf60WtwLL7+HhfN/7e/jWX+/M9ZuXItzZu3JCysJmFhNSvyEuUy1JomIiIi\nInL9aI8RgPenr63cIFLuVDSSq5b96y5Of/A+joICwv4yBtcObZm3/yt+SdqByWDipqj+9K/bC5PR\nVDgmz2xl/Y+HOLzvDCYXI136NKB5u4hi71IZMuQOUlLOEBgYVFGXJSV0oTUtUK1pIiIiIiIi1YqK\nRnJVsn5eR/K8uRhcXYl4fAJHa7nwxS8zyLKcI9InglGN7yLCu1aRMQf3JbPky1/JzbEQFu5Lr8Gx\nBAR5Fjlmz57dvPHGq7z77n/x9fWjZ8/e9OzZuyIvTUpon1rTRERERESuS49M6lnZEUrl4AOj//eq\nZyWmqFpUNJIr4nA4SF+6hLTF32D09ibo0Uf42rqHbbt3YjKYuDl6AP3q9Chyd1F+XgGbVh0mfk8S\nRpOBjj2jadkhEqPx4kLD8uXfs3z5MpYtW8qwYSMq8tKklNSaJiIiIiIiUj2paCSl5rDbOfPZPLLW\nrcElOJjse2/hwzMLOWfJpo5PbUY1votw76LrDZ08ns6aZQfIPptPzQhfug+IISjEu8gxe/bsplmz\n5hgMBsaPn0inTl3o2rV7RV6alJK1wM5OtaaJiMh1qMqt49FgNAAxlZtCRESqGBWNpFTsFgtJs/5L\n9q4duNSuzaaB9dmctBQXg4lb6w+kT2T3IncXWS0FbF5zlL27EjEaDbTrUpcbb21GenpOkXlnz57F\npEkTmTnzA+68cxhubm4qGF3D7p++ush7c34BY15bA8DsSWojFBERERERqQ7KtWj06quv8ttvv2Ew\nGJg8eTItWrQo3LdlyxbefPNNjEYjUVFRvPLKKxiNxvKMI1fJlpND4rtvYz50EFt0HebdYCQ9Zz/1\nfOswsvGd1PIKK3J84olM1nwfz9nMPAKCPelzU2NCavpgMl38/3Pfvv1p27Yd9es3qKjLERERESm1\nuA7LAJjZ+/VKTlI6Ve7OKBGpEh5b/VRlRyiV8ZUdoAoqt6LR1q1b+f3335k/fz5Hjhxh8uTJzJ8/\nv3D/Cy+8wCeffELNmjUZN24c69evp0ePHuUVR66SNT2dU2/NwJJ4ipSYMOa3NmMwuXJb9GB6R3bD\naPijEFRgtfHLz8fYve0kBgO07hhJ+65RmFz+OCY5OZkpU57hb397ikaNYqlTpy7Llq3SQsoiIiIi\nIiIi14hyKxpt3ryZvn37AlC/fn2ysrLIzs7G2/v8OjaLFi0qfB0YGEhGRkZ5RZFS+mNF+YvFNfZl\nVSs7UX71GNn4Tmp6FV38ODnxLKuX7icz3YxfgAe9b4rl/9q79ziZ6/7/44+Z2SO72GUX69weHNaZ\nqChsFCLVRQ6li458VXKlQl3R4RKVootfuioq52IrXSUdUGGRQ07FCi27Dju7LPa8M/P5/cFO9lpW\nys5nZ/d5v93cZuZzfM57x857X/P+vKdWnarFjrNt2xbi45cSEhLKSy+9CqCCkYiIiIiIiBfxtlGX\niQuHmR3B65Ra0SgtLY3Y2Fj349DQUOx2u7tQVHibmprKunXrGD1aA8W8wfdtK/O3yF50rde5yOgi\np8PF5nW/sW3DIQwDWrSvQ8cuV+Hr+/v8Rr/9dpCwsHDCwoLp2bM3ixYtpVu37mY8DRERERERERG5\nBI9NhG0YRrFl6enpjBgxgokTJxISElLi/iEhlfDxsZW4zV8RFhZcasf2NoklrHu117PUDi46uuhY\nyik+XfQTx4+eplpoILcObE3DqBpFtvnhhx+4+eabGTlyJNOmTSMsLJhBg/5WCunlYq7Ua3zTz8c8\nch5vp3bwLLW3Z6m9RURERCqGUisahYeHk5aW5n6cmppKWFiY+3FmZiYPPPAAjz32GJ07d77kjimr\nMQAAIABJREFU8U6ezC6VnHC282u3nym143uD8ycwK2nMl09uIPbcs23ldLrYtuEQW9Yl4XIZNGtd\nm2u7ReLn71OsPRs0aEzz5i1p3Lg5QIVvb0+7Uq/xpGNneHnB1hK30c9Wv1M8Te3tWaXZ3ipGiTfx\ntslfm9Pb7AgiIuKFSq1o1KlTJ/79738zaNAgdu/eTXh4uPuSNIApU6bw97//nRtu0NeqlyV++a4/\ntN2JtCxW/XcP9mNnqBzsR9deTah/Vah7fW5uLq+99jKtWrXhllv6EhgYyH//+5XmLfJiJ07nMmPp\ndvILnIy6vQXtGoddeicRERERERHxWqVWNGrbti2xsbEMGjQIi8XCxIkTiY+PJzg4mM6dO/PJJ5+Q\nlJTE0qVLAejTpw8DBw4srTjyB1TPcNDn+1MlbuNyGWz/8TA/fn8Qp9OgcfOadOoehX+Ab5HtUlIO\n8+ab/6Z585b07t0Hi8WigpEXy8lzMGPpDjIy87mzW5QKRiIiUmF526Svhd7ctMbsCCIi4oVKdU6j\nsWPHFnncpEkT9/1du3aV5qnlMkUdyqXHhjP4OYrPPVUo2zeYTxds41jKaQIr+dKlZ2Maxfw+d9Hp\n06fIysqidu0IIiOjmTdvCe3bd1CxyMs5XS7eWr6bw6mZdG1Th5s71DM7koiIiIiIiHiAxybClrLJ\ncLnYPu/f3LL2NPk+Fj7vXIVf6wcU2WZmt6ns2prCj6sP4Eg5TWSTMK6/KZrASn7ubY4dO8pNN3Ul\nOjqGpUuXY7FY6No1ztNPR64wwzBY+M0+duxPp/lVodzVI1pFQBERERERkQpCRaMKLPd0Bjve+BfV\nfrOTEWTjsxuqcqJa0ZeEb14gyxdt58ihDPwDfOh2SxOimoYXO1bNmrVo27Y9zZu3wOl04uOjl1Z5\n8PXmZFZvTaFuWGVG9muOzWo1O5KIiIiIiIh4iP6yr6CO799NyqwZVDudz7F6Vfikox95fucVBAwI\nsdel1qFmHHFl0DCqOl16xlApyP/sasNgyZKF2O12HnnkMSwWC3PnztcolHJkW6KdJd/uo2qQH48N\naEWgv35diIiIiIiIVCT6K7AC+mX1J7iWfEqQwyClYxR70jsT/dOFt/Xzt9GpezSNm9csUhDKzs5m\n6tR/kZ2dxbBh9xIcXEUFo3Lkt2Oneeuz3fj6WhndvyWhVQIuvZOIiIiIiIiUKyoaVSBOp4PN771O\nSMJunD4WTg28mW49BrNnypqL7jPwvqsJOlcwcDgcJCcfpmHDRlSuXJm3336PWrVqExxcxUPPQDwh\n/VQuMz7aQUGBi4f/1oKGtfTzFRERERERqYhUNKogMjPS2PnGv6h+6CSnq/hSfcQImse0u+R+hQUj\np9NJv369OHr0CN9/v5GgoCDat+9Q2rHFw3LyHMxYup1TWfkMvjGaNtFhZkcSERGRK+jeKavMjiAi\nIl5ERaMK4HDiVlLffJPqZwpIbRBCy0cmUKXa5RUDbDYbnTtfz+HDh3E4CkopqZjJ6XLx5ie7SLZn\nEde2Dt3b1zU7koiIiIiIiJhIRaNybtvXi/FbupJgp4G9UzOuvecf2Gxnf+wOh5NN3x0scf9nnnmK\nF16YgsViYdy4f2reonLKMAwWfJXIroMnaBlZncHdo/WzFhERKYfmjIszO4KUcYn3DzM7wp+WCMS8\n857ZMSoUb369yB+j788upwoc+fzwnxepvORLDAvk3d2PTsOfdBeM0u2ZLHt/K9t/TC7xOG+/PZtd\nu3YAqIhQjq3cdJg1Px2hXngQD90ai82qXw0iIiIiIiIVnUYalUMn0o/wy7+nUDP5NKer+hEx6lEi\nrmoOnB1RsnNzChvW7MfpNGjWJoKftx256LE+//xrWrRo5anoYoIte1P5aPWvVAvyY3T/lgT669eC\niIiISEXnbSN2NOLFHN72OpHLp78Oy5nE3es48/ZcwjIdnGhUg1ajnyEwqBoAWWfyWPX5HpJ/O0lA\nJV9u6tWYhtE16HJzDADjx49l/vz3WbNmPZGR0WY+DfGQA0dO8/ZnP+Pna2N0/1aEnpv4XERERERE\nRERFo3LCMAwSVrxP1U/XEOyE013a0GHIw1htNgAO7LWzZsVe8nId1I8MpVvvJlSq7FfkGNdd15kd\nO7brMrQKIi0jhzeWbqfA6eKRv7WkQa1gsyOJiIiIiIhIGaKiUTmQk5dNwpyp1N2SRL6vBcvQO2nf\nuRcA+XkO1n37K3t2HMPmY+X6m6KJbROBxWLht98OMmPGNCZPfoXAwED69OnHLbfcilXz2ZR72bkO\npi/dwensAu7qEUPrqBpmRxIREREREZEyRkUjL3ck9SC/znyVukeyyKwWQINHHqd6g7OXlh1LOcW3\nn/3C6YxcatQMonvfpoTUqOze97333mXBgg+4+uqODBkyFIvFolFGFYDD6eLNT3ZyJC2L7u3rcmO7\numZHEhERERERkTJIRSMvtv2nbymYu5BaWU5ORdWm1cMT8AsKxuVysWVdElvWJ2EY0Oaa+lx9fUNs\nNiuHDx+iXr36ADzxxHjatm1H3763mfxMxFMMw2D+V3vZ/dtJWkfVYFCc5q4SERERETHLm1PWmB1B\npES6DskLOV1OVn36Jj5vzqNKlpPcG6+h/ZP/wi8omFMnc/hk/k9sXpdE5WB/+g1pzTVdr8Jms7Jw\n4Tw6dmzNypUrAKhcuTK33nq7RhdVIF9uPMT3249Sv2YQD97aDKtVP3sRERERERG5MI008jKnc06R\n8M4UGm0/Sr6vlYDhdxNzTRyGYbBnx1HWfvMrBflOopuFc/1N0fgH+Lr3bdu2PfXrNyAwMNDEZyBm\n2bwnlY/W7Cck2J/R/VsR4Kf//iIi3mbjxo2MHj2a6OizI0VjYmK4//77efLJJ3E6nYSFhfHKK6/g\n5+fH8uXLef/997Fardx5550MGDCAgoICxo0bx5EjR7DZbLz00kvUq1fP5GclIiIjx3U1O8JlSbx/\n2Ll7XU1MIZ6gvxq9yIGUXzg8+w0aHc0hK6QSkaOfoErdRuTmFLBmxV4OJqbh52/jxr5NiYmtyenT\np3j+hX8xYsTD1KtXnyZNmrJu3WZs575RTSqOPUknePu/P+PvZ2N0/5aEBPubHUlERP6kDh068MYb\nb7gfjx8/niFDhtCrVy9ee+01li5dym233casWbNYunQpvr6+9O/fnx49erB69WqqVKnCtGnTWLt2\nLdOmTWP69OkmPhvxNG+9FMbb/qAWESkvdHmaFzAMg4Qfl3Ni6qvUPppDdkw9Wjz3ClXqNuLwwRMs\nefdHDiamUbteVe6892piYmsC8M03X/H227OZOfP3zqAKRhWPPSOHF+dsxOF0MbJfc+rXDDY7koiI\nXEEbN27kxhtvBKBbt24kJCSwfft2WrRoQXBwMAEBAbRt25atW7eSkJBAjx49ALjuuuvYunWrmdFF\nRESkjNNIozIu31nANx//m4Zf78DmBNdNN9Cq/zCcLoN13/zKjs3JWK0WOnZpROuO9UlLs+Ofb8XP\nz4/bb+9PQUEBt9/e3+ynISbJyi1g+kfbOZWZz9CbYmgZWd3sSCIi8hf9+uuvjBgxglOnTvHwww+T\nk5ODn58fANWrV8dut5OWlkZoaKh7n9DQ0GLLrVYrFouF/Px89/4XEhJSCR+f0vvQKSxMH2Z4wo2/\nvgdAp0+XmRvkMj3/+GeA975OvDF34rlbb8temPv3y6a8RNQwAEatetLcHJdp9Llbb3udeDsz2ltF\nozLMnpnKpndeIXqXnQI/K1Xvv5daV3cmPTWTbz77hRP2LKqFBtL91maE1Qpm06aN3H33AB56aBSP\nP/4UFouFgQOHmP00xCQOp4v/9/EujqZnc1uXSLq1rWt2JBER+YsaNmzIww8/TK9evTh8+DD33HMP\nTqfTvd4wjAvud7nLz3fyZPafC/sH2e1nSvX4UpS3trc35g4LC/bK3IW8Obt4jl4nnlVa7V1SMUpF\nozJqd9JW7P95i+jjeeSEBhH92FME1q7L9k2H2fDdAVxOg9i2EVzbLRJf37Of/jVp0oQaNcKoXr2G\nyenFbIZh8MGXe/kl6SRtomswrE8sJ9IzzY4lIiJ/Uc2aNenduzcA9evXp0aNGuzcuZPc3FwCAgI4\nfvw44eHhhIeHk5aW5t4vNTWV1q1bEx4ejt1up0mTJhQUFGAYRomjjKT88bbRDM3pbXYE8RIx77zn\nlYW6b8/NMzYr7mVzg1ymxIXDzI4gHqI5jcoYl+Him/WLyZ02k4jjeeQ1vYoWz7+CKziczxZvZ/2q\n/fj7+9C7fws63RjJO++8yfffrwGgSpWqfP/9RoYNu8/cJyGm+zwhibU7j9KwVjAP9o3FZrWYHUlE\nRK6A5cuX8+677wJgt9tJT0/njjvuYOXKlQB89dVXXH/99bRq1YqdO3dy+vRpsrKy2Lp1K+3bt6dT\np058+eWXAKxevZqOHTua9lxERESk7NNIozIkqyCblfEzaLJqLzYn+PTqTvTtQziQmMZ3XyaSl+ug\nQWR1uvZuTKXKfvzyy888//w/ad26Dddf3wWLxYKPj36kFd2mX44T//0BQqv482j/lvj7afJzEZHy\nIi4ujrFjx/Ltt99SUFDApEmTaNq0KU899RRLliwhIiKC2267DV9fXx5//HHuu+8+LBYLo0aNIjg4\nmN69e7N+/XoGDx6Mn58fU6ZMMfspiYd522iGNzetMTuCiEiFpgpDGXEo4zA/zXmN5j+fpMDPRvUR\nD1KlWTtWr0hk785j+PhYueHmGK5qEkJubiaVKofStGkzZs9+l86dzxaMRH5NPsU7//2FAD8bj/Vv\nRbUgf7MjiYjIFRQUFMTs2bOLLZ87d26xZT179qRnz55FltlsNl566aVSyyciIiLli4pGZcCmfT+Q\nNXceTVLzyatRhejHxpPhqMRHczdzOiOXsFpB3Ni3GXkFp7jxxs40btyUuXPnA9Cv3x0mp5eyIvVk\nNm8s24HLZfB/f2tB3fAgsyOJiIiIiHjEm+fmBhKRK0tFIxMVuBys+OED6ixbS51sF87mMTR5YDTb\ntqaydf1eDAPaXFufqzs3xGaz4nIFULNmLSIiInA4HLoUTdwycwqY/tEOMnMKuKdnY5pfVd3sSCIi\nIiJXjLcWBEaO62p2BBGRv0RVB5OczM1g5bLptPzuN2wuCOjTiyo39GX5sl9IPXKG4Cr+xPVtys6f\nE1i8eC133XUPVquVDz/8BF9fX7PjSxnicLqYFb+TYyey6dmxPl1b1zE7koiIiIiIKbytUJd4/7Bz\n97qamELk4lQ0MkFi2l52vzeLtntO4/D3IfyBERy1RLBy7mYcBS5iYmvSuUc0BY4cxowZRUGBg379\nbicoKFgFIynCMAzeW7GHvYczaNc4jP5dI82OJCIiInLF7OrwBeCFE3h76cgogG+jhp299dLnMGrV\nk2ZHuCyjzQ4gcgkqGnmQYRis+vlLrPPjibUXUBAWQt0Rj5OwOYOD+xLx87fRvW9TqoVb8Q/wwZ9g\nZs16m4iIOgQFBZsdX8qgz9b/xvpdx2hUuwr392mGVROii4iIiJQZ3lw8EhEBFY08JseRy6er3yH6\ns60EZ7uwtGxG0E338MnyA2Rn5RNRvxpdekbx4Ii7SUlJZtWqdfj7+9Ot241mR5cyKmH3MT754SDV\nqwTwaP+W+PvazI4kIiIiUiq8bfRIc3qbHeEv87bLvApfI942Ki1x4bCzt+7L1ETKFhWNPOBo1nG+\nWTaDdmuPYHNBYN++/FapJbs+2YPVauGarlfRqkM9rFYLkZFR2Gw2MjMz8ffX16XLhSUezmDuF78Q\n6G/jsTtbUbWyn9mRREREROQcb72sDn4vXoxa9YW5QUSkTFDRqJRtPbKNgwveoePeLJz+vgQOHsG6\nPU5OpqUQUr0SjWL9WJOwhDbXPAHAc89NxtfXF4suM5KLOH4im5nxOzEM+L/bW1CnRmWzI4mIiIiU\nCm8susDvo168bYQUaI4dT4t55z2zI/wp905ZBcAck3NI6VPRqJQ4XU4+3/4xwR+upIW9AEetGuR0\nu5fvE+y4nAbN29ahY9dG3H57L378cSPdu99MixYt8fPTiBG5uMycAqZ/tJ3MnAKG9WpCbMNQsyOJ\niIiISDnkjQW7sLBg7PYzZscQKVdUNCoFp/PPsOzb2bT84heCc1w4WrVjX2gnjvyYSmBlX9pfX4fm\nrRsC8Mor0zl6NIUWLVqaG1rKvAKHi5nLdnD8ZA69r2nADa0izI4kIiIiIhcwK+5lry1gFM6xIyIC\nYDU7QHlz4NRvLJv/Ah0/3k1QroszcQPY4GzDkcOnaRhVnZRTa7it//UcOZICQLNmsdx4400mp5ay\nzjAM5q74hcTkU7RvEs4dXa4yO5KIiIiIiIiUcxppdIUYhsF3ST+QvmQxnfZlkxdYicMdh3LgUB4+\nvi669IyhaavanFi4jdq1a5ORkUFERB2zY4uX+HTtQTbsPk5kRBXuv6UpVs15JSIiIn+Ct35Dk7fO\n+yIi4u1UNLoC8p35fLRlEXU+XksLewEn6zQmMewGMpPzCKkRwP6ja4iOvRaLxcKQIUMZMGCQ5i6S\nP2zdzqMsX/cbNaoG8MjfWuLnazM7koiIiIiIiFQAKhr9RanZacR/O5sOXx2gUo5BUrNe7C+oCVkF\ntL2uPp+seJN3332LRpF1GDJkKBaLRQUj+cP2HjrJeyv2UMnfh8cGtKJKZb12RERE5PJNiboHgDnj\n4kxOcnm8dWSUiEh5oaLRZbjQV2bG/ppD181nyPOpwk8t+3Ey20blYF963BpL7XrVaNxyAjExjRk4\ncIgJicWbHU3PYmb8TgBG3dGCiBqVTU4kIiIiYg5vLB4losvqRMT7qWj0ByXeP4zRwLdRw4qt+65w\nTuJsCAjO4cUZ9xLZ+n1q17ueatVCGD78fk9GlXLgTHY+Mz7aQVaug3t7N6VpgxCzI4mIiIiIiEgF\no6LRFdSjXzNO5xzCZ5aFM2e87+s1xTz3Tll10XWdW9b2YBIRERGRssNbR+oUjozyxhFSIiLnU9Ho\nCopqGg6Es2XLLgIDA82OIyIiIiIiIiLyp6loVApUMBIRERERqbhi3nmPsLBg7HZdfSAi3k1FIxEP\nOp2Vz5G0LFLSsjhy7l9KWpbZsURERERERESKUdFIpBRcrDiUmVNQZDsLEFYtsNhyEREREREREbOp\naCTyF5zOzueI/VxxKD3Lff9ixaGoOlWJqFGZOjUqE1GjMrWqV8Lf11biRNgiIiIiIiIiZlDR6BIM\nw+DDDxcR/X8P07Zte2KA7OxsKlWqZHY08aArVRwSERERERER8RYqGl3Crl07eOSREbRrdzVffPEN\nFotFBaNyzKzi0JxxcVfoGYiIiIiIiIhcGSoaXYDT6SQ3N5fKlSvTokUrpkyZRo8eN2OxWMyOViaU\ndClVWSp+lJRz+qOdNXJIREREREREpAQqGv2P48ePcc89g2jSpBkzZvw/AO699wGTU3kPwzAwzt26\nXOceG+AyjLPLDPDLzONUVr573dltDVz8fr9w+e/7nr11/c8+7vuA4Tp7fOPcdiV57I21RR6rOCQi\nIiIiIiJSVKkWjSZPnsz27duxWCxMmDCBli1butetX7+e1157DZvNxg033MCoUaNKM8ofFhpanYIC\nB3l5eTgcDnx8Sreu5slROw6ni7wCJ3n5TnLznb/fP3ebV3Bueb6D3AIn+fkucgscRbfJd5Z4jvum\nrr6imUtL66gaKg6JiIiIiIj8Bd76hT5l6QqZsq7UKiKbNm0iKSmJJUuWsH//fiZMmMCSJUvc6198\n8UXeffddatasyd13383NN99MVFRUacUp0bp169i79yB9+/bD19eX5cu/JCgoyJQs5zuVmecu1pxf\n5HEXd9xFnnPr3Ns6imxTuN7hLHn0zaVYAH+/kgsrMfWqYbWAxWL5/dZqwcLv9wMCfMjPd2K1gNVi\nweLe/vz7YLFasHJ2mdX6+zqLez9L8WMUboeFD1f/etGcj/ZvedF1IiIiIiIiIlKKRaOEhAS6d+8O\nQGRkJKdOnSIzM5OgoCAOHz5M1apVqV27NgBdunQhISHBlKJRZuYZbrnlFiwWC9263UhQUFCZKBgB\njJm57k/v6+tjxd/XRoCfjWrB/gT42vD3s+F/7vb8xwF+PufuW/H39SHAr/g2/n42/HysWCyWEqvJ\n4+5qe8lsYWHB2O1n/vRz+6NKKhqJiIiIiIjInzNnXJzH/q67krx1ZJSZSq1olJaWRmxsrPtxaGgo\ndrudoKAg7HY7oaGhRdYdPny4xOOFhFTCx+fKXz4UFhbM22+/TZ06dWjUqPYVP/5fcX3rOgT42Qj0\nP1vUCfT3IcDPh0B/GwHn7gf4nb1/dl3htj7YrOZM2h0WFnxFtystZp/fDBXxOZtJ7e1Zam/PUnuL\niIiIVAwemwjbuMTExJdy8mT2FUpS3IABA7Dbz5S5Kunwno0vY2sDV76DrHwHWaWW6NL+SBuWhYq0\n2ef3tLLQ5hWJ2tuz1N6eVZrtrWKUiIiISNlSakWj8PBw0tLS3I9TU1MJCwu74Lrjx48THh5eWlHk\nCvOWScO8JaeIiIiIiIhIWWQtrQN36tSJlStXArB7927Cw8PdcwXVrVuXzMxMkpOTcTgcrF69mk6d\nOpVWFBERERERERERuUylNtKobdu2xMbGMmjQICwWCxMnTiQ+Pp7g4GB69OjBpEmTePzxxwHo3bs3\njRo1Kq0oZZpGw4iIiIiIiIhIWVSqcxqNHTu2yOMmTZq471999dUsWbKkNE8vIiIiInJF6Zt3RESk\nIim1y9NERERERERERMR7eezb00REREREygtvnGJA3zYpIiKXSyONRERERERERESkGBWNRERERERE\nRESkGBWNRERERERERESkGM1pJCIiIlLBTJ48me3bt2OxWJgwYQItW7Y0O5KIiIiUQSoaiYiIiFQg\nmzZtIikpiSVLlrB//34mTJjAkiVLzI4lIiIiZZCKRiIiIiIVSEJCAt27dwcgMjKSU6dOkZmZSVBQ\nkCl57p2yypTziohIxeVt7z1XmzizkOY0EhEREalA0tLSCAkJcT8ODQ3FbrebmEhERETKKq8ZaRQW\nFuzVx5ei1N6epzb3LLW3Z6m9PUvtXb4YhlHi+tL6eT87rW+pHFdKpv+/nqX29jy1uWd5W3t/Nq2f\n2RG8jkYaiYiIiFQg4eHhpKWluR+npqYSFhZmYiIREREpq1Q0EhEREalAOnXqxMqVKwHYvXs34eHh\nps1nJCIiImWb11yeJiIiIiJ/Xdu2bYmNjWXQoEFYLBYmTpxodiQREREpoyzGpS5kFxERERERERGR\nCkeXp4mIiIiIiIiISDEqGomIiIiIiIiISDEVrmg0efJkBg4cyKBBg9ixY0eRdevXr6d///4MHDiQ\nWbNmmZSwfCmpvTds2MCdd97JoEGDGD9+PC6Xy6SU5UdJ7V1o2rRpDB061MPJyqeS2vvo0aMMHjyY\n/v378+yzz5qUsPwpqc0XLFjAwIEDGTx4MP/6179MSli+JCYm0r17d+bPn19snd4z5XKo/+V56oN5\nlvpgnqU+mGep/+V5ZaoPZlQgGzduNB588EHDMAzj119/Ne68884i63v16mUcOXLEcDqdxuDBg419\n+/aZEbPcuFR79+jRwzh69KhhGIbxyCOPGGvWrPF4xvLkUu1tGIaxb98+Y+DAgcbdd9/t6XjlzqXa\n+9FHHzW++uorwzAMY9KkSUZKSorHM5Y3JbX5mTNnjG7duhkFBQWGYRjG8OHDjW3btpmSs7zIysoy\n7r77buOZZ54x5s2bV2y93jPlj1L/y/PUB/Ms9cE8S30wz1L/y/PKWh+sQo00SkhIoHv37gBERkZy\n6tQpMjMzATh8+DBVq1aldu3aWK1WunTpQkJCgplxvV5J7Q0QHx9PrVq1AAgNDeXkyZOm5CwvLtXe\nAFOmTGHMmDFmxCt3Smpvl8vFli1biIuLA2DixIlERESYlrW8KKnNfX198fX1JTs7G4fDQU5ODlWr\nVjUzrtfz8/Pj7bffJjw8vNg6vWfK5VD/y/PUB/Ms9cE8S30wz1L/y/PKWh+sQhWN0tLSCAkJcT8O\nDQ3FbrcDYLfbCQ0NveA6+XNKam+AoKAgAFJTU1m3bh1dunTxeMby5FLtHR8fT4cOHahTp44Z8cqd\nktr7xIkTVK5cmZdeeonBgwczbdo0s2KWKyW1ub+/P6NGjaJ79+5069aNVq1a0ahRI7Oilgs+Pj4E\nBARccJ3eM+VyqP/leeqDeZb6YJ6lPphnqf/leWWtD1ahikb/yzAMsyNUKBdq7/T0dEaMGMHEiROL\n/DKSv+789s7IyCA+Pp7hw4ebmKh8O7+9DcPg+PHj3HPPPcyfP5+ff/6ZNWvWmBeunDq/zTMzM3nr\nrbf48ssv+fbbb9m+fTt79uwxMZ2IXIz6X56nPphnqQ/mWeqDeZb6XxVPhSoahYeHk5aW5n6cmppK\nWFjYBdcdP378gsPB5I8rqb3h7C+ZBx54gMcee4zOnTubEbFcKam9N2zYwIkTJ7jrrrt4+OGH2b17\nN5MnTzYrarlQUnuHhIQQERFB/fr1sdlsXHvttezbt8+sqOVGSW2+f/9+6tWrR2hoKH5+frRv355d\nu3aZFbXc03umXA71vzxPfTDPUh/Ms9QH8yz1v8oWM943K1TRqFOnTqxcuRKA3bt3Ex4e7h6eW7du\nXTIzM0lOTsbhcLB69Wo6depkZlyvV1J7w9lru//+979zww03mBWxXCmpvXv27MkXX3zBhx9+yMyZ\nM4mNjWXChAlmxvV6JbW3j48P9erV47fffnOv11Ddv66kNq9Tpw779+8nNzcXgF27dtGwYUOzopZ7\nes+Uy6H+l+epD+ZZ6oN5lvpgnqX+V9lixvumxahgY4RfffVVNm/ejMViYeLEifz8888EBwfTo0cP\nfvzxR1599VUAbrrpJu677z6T03q/i7V3586dufrqq2nTpo172z59+jBw4EAT03q/kl45oUT0AAAK\nVElEQVTfhZKTkxk/fjzz5s0zMWn5UFJ7JyUlMW7cOAzDICYmhkmTJmG1Vqg6fakoqc0XL15MfHw8\nNpuNNm3a8OSTT5od16vt2rWLqVOnkpKSgo+PDzVr1iQuLo66devqPVMum/pfnqc+mGepD+ZZ6oN5\nlvpfnlXW+mAVrmgkIiIiIiIiIiKXppKriIiIiIiIiIgUo6KRiIiIiIiIiIgUo6KRiIiIiIiIiIgU\no6KRiIiIiIiIiIgUo6KRiIiIiIiIiIgUo6KRSBmVnJxM48aNWbRoUZHlmzdvpnHjxmzcuNGkZJcn\nKSmJuLi4i6632+08+uijABw/fpyEhITLOv7gwYOveFts3LiRwYMHX9Y+jRs3xuFwFFs+ZswYjh8/\nTnx8PGPHji2yDODTTz/964FFRETkiimPfbD//Oc/rFmz5qLbqg8mIhejopFIGdawYUPi4+OLLIuP\nj6dRo0YmJbrywsLCeOONN4CzHYUNGzaYnOjKev3116lZs+YFlx0/fpzFixeblExEREQuprz1wR58\n8EG6du160fXqg4nIxfiYHUBELi48PJy8vDz27dtHdHQ0OTk5bNmyhVatWrm3+eKLL5g/fz6GYRAa\nGsqLL75ISEgICxcu5NNPP8XX1xd/f39ef/11qlSpQlxcHPfccw/ff/89ycnJPPfcc1x77bVFzjt0\n6FCaNWvGvn37sNvtPPTQQ/Tp04dx48bh5+fHwYMHefXVVzl58iRTp07F4XBQUFDAs88+S7Nmzdi6\ndSsTJ04kNDSU2NhY93HT09MZP348Z86cwWaz8eyzz1KpUiWGDBnCggULmD59OoZhUK1aNe666y6e\nf/55kpKSyMrKok+fPtx7773k5OQwZswYTp48SYMGDcjLyyvWbhs3bmT69OlERESQkpJCcHAwr7/+\nOhkZGYwcOZKYmBiio6N54IEHmDx5Mrt37wbgmmuu4bHHHgMgPz+fJ598kkOHDlG5cmVmzJhBUFAQ\nM2bMcH8SV6tWLV555RV8fX0BmD17Nhs2bCArK4upU6cSExNDXFwcc+fOLZKvcNnTTz9NYmKi+zxj\nxoyhY8eOANx///0MHTqULl26/NWXkYiIiFym8tYHGzduHO3atWPAgAF89NFHLFq0CF9fXzp27MiA\nAQPUB1MfTOSiNNJIpIzr168fy5YtA2DlypXccMMNWK1n/+sePXqU2bNn895777Fo0SI6dOjAW2+9\nBUBeXh7vvvsu8+fPp06dOixfvtx9TH9/f+bMmcPIkSP54IMPLnheh8PBnDlzmDlzJpMnT8blcgGQ\nnZ3NvHnzqFmzJk888QTPPfcc8+bNY9KkSTzzzDMAvPzyy4wdO5b333+fsLAw9zGnTZtGly5dWLRo\nEY8++miRYcH16tXj9ttv59Zbb2X48OF88MEHhIeHM2/ePD766CM+//xz9uzZw/LlywkICGDJkiWM\nHTuWffv2XTD/7t27efLJJ1m8eDHVqlVzf1q4f/9+Ro0axYgRI1ixYgXJycksWrSIBQsWsG7dOjZt\n2gRAYmIi//jHP1i8eDGhoaF88sknOBwOAgMDWbhwIYsXL+bMmTOsXbvWfc7IyEjmz5/PkCFDmDlz\n5iV/to888ggxMTG8/PLLDBo0iI8//hiAjIwMDh48yPXXX3/JY4iIiEjpKE99sEIpKSnMnj2bhQsX\nsmTJElJTUykoKFAfTH0wkYvSSCORMq5Xr17cfvvtjB07lo8//pixY8eyYMECALZt24bdbue+++4D\nzn4yU7duXQCqVavGgw8+iNVqJSUlpUjHoUOHDgBERERw6tSpC563c+fOADRo0ACLxUJ6ejoAbdq0\nAc6OGjp48CBPP/20e5/MzExcLhd79+6lXbt2wNlPjubNmwfAjh07GD58uDtDhw4dSE5OvuD5N27c\nyLFjx/jxxx/dz+3QoUMkJia6jx0eHs5VV111wf2joqLcQ5Lbtm3LL7/8QlxcHFWrVnXvs337dq69\n9losFgs2m4327duzc+dOmjdvzlVXXUWtWrXcz3nv3r34+PhgtVoZMmQIPj4+HDhwgJMnT7rP2alT\nJ/f55syZc8FcF9OrVy+mT59OVlYWX3/9NX379nV3TEVERMTzylMfrNDOnTuJjY0lICAAgClTphQ7\nv/pg6oOJnE9FI5EyLjQ0lGbNmrF06VLsdjstWrRwr/Pz86Nly5buT7YKHTt2jKlTp/L5559TvXp1\npk6dWmS9j8/v//UNw7jgeQs/1SrcxmKxuM9ZeOvr61usM1Ko8M3W6XS6l1ksliLHLYmfnx+jRo2i\nZ8+eRZZv2LChyBv5xY53/vM6P3/hMObCPP+7T+Gy889RuHzLli0sW7aMZcuWUalSJfcE3oUK9zn/\nOH+Uv78/PXr04Ouvv2blypVMnDjxsvYXERGRK6s89cEKWSyWi573/OemPpiIFFIJVcQL9OvXj9df\nf51bbrmlyPIWLVqwY8cO7HY7ACtWrOCbb74hPT2dkJAQqlevTkZGBmvXriU/P/+yzlk4GeLBgwex\nWq2EhoYWWR8cHEzdunX57rvv3NsVDgeOjIzkp59+AmD9+vXufdq0acMPP/wAnP0GkqeeeqrIMS0W\ni/vbL9q1a8eKFSuAs52Sl156iYyMDCIjI9m2bRtwdmj4wYMHL5j/wIEDpKamArBlyxYaN25cbJvW\nrVuzfv16DMPA4XCwadMm91wFBw4ccH+7xtatW4mJiSE9PZ06depQqVIlUlJS+Omnn4q0a+F19oXb\nX4rVai3ybR8DBw5k0aJFGIZBvXr1Lrm/iIiIlK7y0gf739yZmZkAjB49ml27dqkPpj6YyEVppJGI\nF4iLi+PZZ5/l1ltvLbK8Zs2aPP300zz00EMEBgYSEBDA1KlTCQ0NpUGDBvTv35/69evz6KOPMmnS\npMua0M/hcDBy5EiSk5P55z//ecFhulOnTuXFF1/kP//5Dw6Hg3HjxgHwxBNP8MILL1C7dm2aNWvm\n3n706NGMHz+e1atXA/DPf/6zyPHat2/PmDFj8PX1ZeTIkezbt4+BAwfidDrp2rUr1apVo1+/fqxa\ntYohQ4ZQt27dIp/6nS8qKorXXnuNpKQkqlatym233caJEyeKbNOzZ0+2bt3K4MGDcblcdO/enXbt\n2rFx40aaNWvG9OnTSUpKIigoiH79+gEwZ84cBg8eTHR0NI888gizZs2iY8eO2Gw29u3bx+LFizl5\n8iSvvPLKJds4KiqK9PR0hg8fzty5c4mKisLpdHLHHXdccl8REREpfeWlD1YoIiKChx9+mGHDhuHj\n40Pbtm1p3rw5mZmZ6oOpDyZyQRbjUuMTRaTCGTp0KCNHjuS6664zO8qfUvjNHYsWLTI7ymVJTk7m\nwQcfdH/jioiIiFQs6oOZQ30wkYvTSCMRkTJg9uzZfPHFF7zwwgvqrIiIiIh4iPpgIiXTSCMRERER\nERERESlGE2GLiIiIiIiIiEgxKhqJiIiIiIiIiEgxKhqJiIiIiIiIiEgxKhqJiIiIiIiIiEgxKhqJ\niIiIiIiIiEgxKhqJiIiIiIiIiEgx/x9u+9AtLLk7jQAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7f4120cd9390>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"8KcIlAoaf_9H","colab_type":"code","outputId":"dfe59b88-a480-4b57-c0da-b0bf706b1672","executionInfo":{"status":"ok","timestamp":1547540499028,"user_tz":-480,"elapsed":6788,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":170}},"cell_type":"code","source":["name_svc = [\"SVC\",\"SVC+isotonic\",\"SVC+sigmoid\"]\n","\n","svc = SVC(kernel = \"linear\",gamma=1)\n","\n","models_svc = [svc\n","              ,CalibratedClassifierCV(svc, cv=2, method='isotonic')\n","              ,CalibratedClassifierCV(svc, cv=2, method='sigmoid')]\n","\n","for clf, name in zip(models_svc,name_svc):\n","    clf.fit(Xtrain,Ytrain)\n","    y_pred = clf.predict(Xtest)\n","    if hasattr(clf, \"predict_proba\"):\n","        prob_pos = clf.predict_proba(Xtest)[:, 1]\n","    else:\n","        prob_pos = clf.decision_function(Xtest)\n","        prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())\n","    clf_score = brier_score_loss(Ytest, prob_pos, pos_label=y.max())\n","    score = clf.score(Xtest,Ytest)\n","    print(\"{}:\".format(name))\n","    print(\"\\tBrier:{:.4f}\".format(clf_score))\n","    print(\"\\tAccuracy:{:.4f}\".format(score))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["SVC:\n","\tBrier:0.1630\n","\tAccuracy:0.8633\n","SVC+isotonic:\n","\tBrier:0.0999\n","\tAccuracy:0.8639\n","SVC+sigmoid:\n","\tBrier:0.0987\n","\tAccuracy:0.8634\n"],"name":"stdout"}]},{"metadata":{"id":"Pyo7-FRsmmIS","colab_type":"text"},"cell_type":"markdown","source":["# 多项式朴素贝叶斯以及其变化"]},{"metadata":{"id":"MA-3_BN0f_9K","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.preprocessing import MinMaxScaler\n","from sklearn.naive_bayes import MultinomialNB\n","from sklearn.model_selection import train_test_split\n","from sklearn.datasets import make_blobs\n","from sklearn.metrics import brier_score_loss"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Rve-btpAf_9L","colab_type":"code","colab":{}},"cell_type":"code","source":["class_1 = 500 \n","class_2 = 500 #两个类别分别设定500个样本\n","centers = [[0.0, 0.0], [2.0, 2.0]] #设定两个类别的中心\n","clusters_std = [0.5, 0.5] #设定两个类别的方差\n","X, y = make_blobs(n_samples=[class_1, class_2],\n","                  centers=centers,\n","                  cluster_std=clusters_std,\n","                  random_state=0, shuffle=False)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"OhKfAg8Vf_9M","colab_type":"code","outputId":"217c0b05-052f-44bb-da69-f7a3b94225f0","executionInfo":{"status":"ok","timestamp":1547604024310,"user_tz":-480,"elapsed":988,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["X.shape"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1000, 2)"]},"metadata":{"tags":[]},"execution_count":3}]},{"metadata":{"id":"lQvalLPYY4iP","colab_type":"code","colab":{}},"cell_type":"code","source":["import numpy as np"],"execution_count":0,"outputs":[]},{"metadata":{"id":"LMJo-HLEf_9X","colab_type":"code","outputId":"ee456733-0245-40d7-e191-b24404eaeb5f","executionInfo":{"status":"ok","timestamp":1547604048549,"user_tz":-480,"elapsed":490,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["np.unique(y)"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1])"]},"metadata":{"tags":[]},"execution_count":6}]},{"metadata":{"id":"mch7Vdsbf_9Z","colab_type":"code","colab":{}},"cell_type":"code","source":["Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y\n","                                                ,test_size=0.3\n","                                                ,random_state=420)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"cpZdzPWrf_9a","colab_type":"code","colab":{}},"cell_type":"code","source":["mms = MinMaxScaler().fit(Xtrain) #训练集上来实例化和训练我们的模型\n","# 为了保证数据中没有负数\n","Xtrain_ = mms.transform(Xtrain)\n","\n","Xtest_ = mms.transform(Xtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"5zttygm9f_9a","colab_type":"code","colab":{}},"cell_type":"code","source":["mnb = MultinomialNB().fit(Xtrain_, Ytrain)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"HMGh4Mgxf_9b","colab_type":"code","outputId":"6461e857-822c-414e-aa7b-31fccf1e59d7","executionInfo":{"status":"ok","timestamp":1547604127210,"user_tz":-480,"elapsed":767,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["#重要属性：调用根据数据获取的，每个标签类的对数先验概率log(P(Y))\n","#由于概率永远是在[0,1]之间，因此对数先验概率返回的永远是负值\n","mnb.class_log_prior_"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([-0.69029411, -0.69600841])"]},"metadata":{"tags":[]},"execution_count":10}]},{"metadata":{"id":"hDeFOQJWf_9c","colab_type":"code","outputId":"fda20002-20a0-4f17-fc6f-33b35f154657","executionInfo":{"status":"ok","timestamp":1547604130953,"user_tz":-480,"elapsed":697,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":1343}},"cell_type":"code","source":["Ytrain == 1 #计数：所有标签类别=1的样本量"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ True, False, False, False,  True,  True, False,  True,  True,\n","        True,  True,  True, False,  True,  True,  True,  True,  True,\n","        True,  True,  True, False,  True,  True, False, False, False,\n","        True, False,  True, False,  True, False,  True, False,  True,\n","       False, False, False, False,  True,  True,  True, False, False,\n","        True,  True, False,  True, False, False, False, False, False,\n","        True,  True, False, False, False, False, False, False, False,\n","        True,  True, False, False,  True,  True,  True,  True, False,\n","        True, False, False, False, False,  True, False, False, False,\n","        True,  True,  True,  True,  True,  True,  True,  True,  True,\n","        True, False, False,  True, False, False,  True, False,  True,\n","       False,  True, False,  True,  True,  True,  True,  True, False,\n","       False, False,  True,  True, False, False, False,  True,  True,\n","        True,  True, False, False,  True,  True,  True, False, False,\n","       False,  True,  True, False, False, False, False, False,  True,\n","        True, False,  True,  True, False,  True, False, False, False,\n","       False, False, False,  True,  True, False,  True,  True,  True,\n","       False, False, False, False, False, False,  True,  True,  True,\n","       False, False,  True, False, False,  True, False,  True, False,\n","       False, False, False, False, False, False,  True,  True,  True,\n","       False, False, False, False, False,  True, False, False,  True,\n","       False, False, False,  True, False, False, False,  True,  True,\n","        True,  True, False,  True, False, False,  True, False,  True,\n","        True, False,  True,  True, False,  True,  True, False,  True,\n","        True,  True,  True, False,  True, False,  True, False,  True,\n","       False, False, False, False,  True,  True,  True, False,  True,\n","        True,  True,  True, False, False, False, False, False,  True,\n","       False,  True, False,  True, False,  True,  True, False,  True,\n","        True,  True,  True, False,  True, False,  True,  True, False,\n","       False, False, False, False,  True, False,  True, False,  True,\n","       False, False,  True, False, False,  True, False,  True,  True,\n","       False, False,  True,  True, False, False,  True,  True, False,\n","        True, False,  True,  True,  True,  True, False,  True,  True,\n","       False, False,  True, False,  True, False, False, False, False,\n","        True, False,  True,  True, False, False, False,  True, False,\n","        True,  True,  True, False, False,  True,  True,  True, False,\n","        True, False,  True,  True,  True, False,  True,  True,  True,\n","       False,  True, False, False, False,  True, False, False, False,\n","       False,  True, False, False,  True,  True,  True, False, False,\n","        True, False,  True,  True, False, False,  True, False,  True,\n","       False,  True, False, False,  True,  True,  True,  True, False,\n","        True,  True, False,  True,  True, False,  True,  True,  True,\n","        True, False,  True, False, False, False, False,  True,  True,\n","       False, False, False,  True, False, False, False,  True, False,\n","        True,  True, False, False,  True,  True,  True, False, False,\n","       False, False, False,  True, False, False,  True,  True, False,\n","        True,  True,  True, False, False,  True, False,  True,  True,\n","        True,  True, False, False, False, False,  True,  True,  True,\n","       False, False,  True, False,  True, False,  True,  True, False,\n","       False, False,  True, False,  True, False,  True,  True, False,\n","        True, False, False, False,  True, False,  True,  True,  True,\n","        True, False, False, False,  True,  True, False,  True,  True,\n","       False, False,  True, False,  True, False,  True, False, False,\n","        True, False,  True, False, False, False, False, False,  True,\n","        True,  True,  True, False,  True, False, False,  True,  True,\n","       False, False, False, False,  True,  True, False, False, False,\n","       False, False, False,  True,  True,  True, False,  True,  True,\n","        True, False, False, False,  True, False,  True, False, False,\n","       False,  True, False,  True, False, False, False, False,  True,\n","       False, False, False,  True,  True,  True, False,  True,  True,\n","       False, False, False,  True, False,  True,  True,  True,  True,\n","       False,  True, False,  True,  True, False, False, False,  True,\n","        True, False, False,  True, False,  True, False,  True,  True,\n","        True, False, False, False, False,  True,  True,  True,  True,\n","        True,  True, False,  True, False,  True,  True,  True, False,\n","        True,  True, False, False, False,  True, False,  True,  True,\n","       False,  True, False,  True, False, False, False,  True,  True,\n","       False,  True,  True,  True, False,  True,  True, False,  True,\n","       False, False, False, False, False,  True,  True,  True,  True,\n","        True,  True, False,  True,  True, False,  True,  True, False,\n","        True, False,  True,  True,  True, False,  True, False,  True,\n","        True, False,  True, False,  True,  True, False, False, False,\n","       False,  True,  True, False,  True, False, False, False,  True,\n","       False,  True, False,  True,  True, False, False,  True, False,\n","       False, False,  True, False, False,  True, False,  True, False,\n","        True,  True,  True, False, False,  True,  True, False,  True,\n","        True, False,  True,  True,  True,  True, False, False, False,\n","        True,  True,  True,  True, False, False,  True])"]},"metadata":{"tags":[]},"execution_count":11}]},{"metadata":{"id":"cSR0Olqqf_9d","colab_type":"code","outputId":"bc07e45c-b526-43e9-ce65-d1fca237ceec","executionInfo":{"status":"ok","timestamp":1547604137087,"user_tz":-480,"elapsed":854,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["(Ytrain == 0).sum()/Ytrain.shape[0]"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.5014285714285714"]},"metadata":{"tags":[]},"execution_count":12}]},{"metadata":{"id":"wCyizU1Jf_9h","colab_type":"code","outputId":"bd087f19-2636-42fe-9785-1c5989596e85","executionInfo":{"status":"ok","timestamp":1547540590619,"user_tz":-480,"elapsed":997,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["mnb.class_log_prior_.shape #永远等于标签中所带的类别数量"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2,)"]},"metadata":{"tags":[]},"execution_count":112}]},{"metadata":{"id":"tR4ZfImkf_9i","colab_type":"code","outputId":"3453b104-0e29-47f1-f01f-da8f91f5668d","executionInfo":{"status":"ok","timestamp":1547604169559,"user_tz":-480,"elapsed":706,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["#可以使用np.exp来查看真正的概率值\n","np.exp(mnb.class_log_prior_)"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.50142857, 0.49857143])"]},"metadata":{"tags":[]},"execution_count":13}]},{"metadata":{"id":"9FEgUuJtf_9k","colab_type":"code","outputId":"ed080ab2-aedb-43db-fbe9-f6e8a2d0ecde","executionInfo":{"status":"ok","timestamp":1547604208080,"user_tz":-480,"elapsed":695,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"cell_type":"code","source":["#重要属性：返回一个固定标签类别下的每个特征的对数概率log(P(Xi|y))\n","mnb.feature_log_prob_"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.76164788, -0.62903951],\n","       [-0.72500918, -0.6622691 ]])"]},"metadata":{"tags":[]},"execution_count":14}]},{"metadata":{"id":"WopBbkfvf_9l","colab_type":"code","outputId":"75f20631-c9c9-4020-f334-5be93e292688","executionInfo":{"status":"ok","timestamp":1547604210933,"user_tz":-480,"elapsed":703,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["mnb.feature_log_prob_.shape #2个特征，2个标签"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2, 2)"]},"metadata":{"tags":[]},"execution_count":15}]},{"metadata":{"id":"HGLf8UOwf_9n","colab_type":"code","outputId":"1c24412c-3d75-4a12-f5fe-04283b3a50b5","executionInfo":{"status":"ok","timestamp":1547604257677,"user_tz":-480,"elapsed":875,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["#重要属性：在fit时每个标签类别下包含的样本数\n","#当fit接口中的sample_weight被设置时，该接口返回的值也会受到加权的影响\n","mnb.class_count_\n","# 训练集700个样本，351个标签为0样本，349个标签为1样本"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([351., 349.])"]},"metadata":{"tags":[]},"execution_count":16}]},{"metadata":{"id":"l4fn9VPsf_9o","colab_type":"code","outputId":"e932e007-912f-4fc5-e908-662525c581e3","executionInfo":{"status":"ok","timestamp":1547604272959,"user_tz":-480,"elapsed":704,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["mnb.class_count_.shape #返回和我们的标签类别一样的结构"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2,)"]},"metadata":{"tags":[]},"execution_count":17}]},{"metadata":{"id":"IaxzbDlyf_9q","colab_type":"code","outputId":"31db88e5-da1f-4aaf-fc02-5a293403fb87","executionInfo":{"status":"ok","timestamp":1547604364676,"user_tz":-480,"elapsed":793,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":255}},"cell_type":"code","source":["#一些传统的接口\n","mnb.predict(Xtest_)"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n","       1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n","       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1,\n","       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n","       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n","       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"]},"metadata":{"tags":[]},"execution_count":18}]},{"metadata":{"id":"VO6s9724f_9s","colab_type":"code","outputId":"01934f89-a704-40c7-bf56-795be749c3cc","executionInfo":{"status":"ok","timestamp":1547604367786,"user_tz":-480,"elapsed":694,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":5117}},"cell_type":"code","source":["mnb.predict_proba(Xtest_) #每个样本在每个标签取值下的概率"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.49847128, 0.50152872],\n","       [0.50065987, 0.49934013],\n","       [0.50122363, 0.49877637],\n","       [0.50183745, 0.49816255],\n","       [0.50146433, 0.49853567],\n","       [0.50153147, 0.49846853],\n","       [0.50204549, 0.49795451],\n","       [0.50033124, 0.49966876],\n","       [0.50105254, 0.49894746],\n","       [0.50182815, 0.49817185],\n","       [0.50270707, 0.49729293],\n","       [0.50133396, 0.49866604],\n","       [0.49820896, 0.50179104],\n","       [0.50342829, 0.49657171],\n","       [0.50099022, 0.49900978],\n","       [0.49974388, 0.50025612],\n","       [0.50423879, 0.49576121],\n","       [0.50449207, 0.49550793],\n","       [0.49818224, 0.50181776],\n","       [0.50245485, 0.49754515],\n","       [0.50393627, 0.49606373],\n","       [0.50193571, 0.49806429],\n","       [0.49996152, 0.50003848],\n","       [0.50460038, 0.49539962],\n","       [0.50261175, 0.49738825],\n","       [0.50140163, 0.49859837],\n","       [0.50332522, 0.49667478],\n","       [0.50122253, 0.49877747],\n","       [0.50409939, 0.49590061],\n","       [0.49998717, 0.50001283],\n","       [0.50179417, 0.49820583],\n","       [0.5000057 , 0.4999943 ],\n","       [0.50190643, 0.49809357],\n","       [0.50022071, 0.49977929],\n","       [0.50097776, 0.49902224],\n","       [0.50305244, 0.49694756],\n","       [0.50109969, 0.49890031],\n","       [0.50159754, 0.49840246],\n","       [0.50166181, 0.49833819],\n","       [0.5013594 , 0.4986406 ],\n","       [0.50138322, 0.49861678],\n","       [0.50174823, 0.49825177],\n","       [0.50346533, 0.49653467],\n","       [0.49918461, 0.50081539],\n","       [0.50097953, 0.49902047],\n","       [0.50178893, 0.49821107],\n","       [0.49990491, 0.50009509],\n","       [0.50202801, 0.49797199],\n","       [0.50176739, 0.49823261],\n","       [0.50254336, 0.49745664],\n","       [0.50116963, 0.49883037],\n","       [0.49920141, 0.50079859],\n","       [0.49944211, 0.50055789],\n","       [0.50031645, 0.49968355],\n","       [0.50153599, 0.49846401],\n","       [0.50352461, 0.49647539],\n","       [0.50049178, 0.49950822],\n","       [0.49983178, 0.50016822],\n","       [0.50289574, 0.49710426],\n","       [0.50193747, 0.49806253],\n","       [0.49961192, 0.50038808],\n","       [0.50198825, 0.49801175],\n","       [0.50289658, 0.49710342],\n","       [0.50218643, 0.49781357],\n","       [0.49888163, 0.50111837],\n","       [0.49920224, 0.50079776],\n","       [0.50151585, 0.49848415],\n","       [0.50230998, 0.49769002],\n","       [0.50400072, 0.49599928],\n","       [0.50138781, 0.49861219],\n","       [0.50001327, 0.49998673],\n","       [0.49982061, 0.50017939],\n","       [0.50133343, 0.49866657],\n","       [0.503153  , 0.496847  ],\n","       [0.50180689, 0.49819311],\n","       [0.50141445, 0.49858555],\n","       [0.50248588, 0.49751412],\n","       [0.50308391, 0.49691609],\n","       [0.50024574, 0.49975426],\n","       [0.50173534, 0.49826466],\n","       [0.50018764, 0.49981236],\n","       [0.50171743, 0.49828257],\n","       [0.501695  , 0.498305  ],\n","       [0.50357487, 0.49642513],\n","       [0.50245246, 0.49754754],\n","       [0.50233437, 0.49766563],\n","       [0.50190721, 0.49809279],\n","       [0.49839991, 0.50160009],\n","       [0.50221084, 0.49778916],\n","       [0.5012638 , 0.4987362 ],\n","       [0.50199765, 0.49800235],\n","       [0.50100067, 0.49899933],\n","       [0.50122519, 0.49877481],\n","       [0.50358523, 0.49641477],\n","       [0.50353492, 0.49646508],\n","       [0.50060048, 0.49939952],\n","       [0.50261115, 0.49738885],\n","       [0.50170128, 0.49829872],\n","       [0.50084485, 0.49915515],\n","       [0.49977894, 0.50022106],\n","       [0.50134207, 0.49865793],\n","       [0.49967049, 0.50032951],\n","       [0.50394134, 0.49605866],\n","       [0.50143828, 0.49856172],\n","       [0.50356078, 0.49643922],\n","       [0.50332876, 0.49667124],\n","       [0.50095206, 0.49904794],\n","       [0.50220001, 0.49779999],\n","       [0.50256184, 0.49743816],\n","       [0.50166902, 0.49833098],\n","       [0.50254134, 0.49745866],\n","       [0.5004789 , 0.4995211 ],\n","       [0.50302788, 0.49697212],\n","       [0.50052704, 0.49947296],\n","       [0.50219724, 0.49780276],\n","       [0.50026189, 0.49973811],\n","       [0.50095584, 0.49904416],\n","       [0.50206864, 0.49793136],\n","       [0.50010878, 0.49989122],\n","       [0.50109718, 0.49890282],\n","       [0.50162885, 0.49837115],\n","       [0.50020595, 0.49979405],\n","       [0.50067955, 0.49932045],\n","       [0.50280886, 0.49719114],\n","       [0.50237353, 0.49762647],\n","       [0.50222485, 0.49777515],\n","       [0.5001119 , 0.4998881 ],\n","       [0.50112275, 0.49887725],\n","       [0.50362029, 0.49637971],\n","       [0.50054287, 0.49945713],\n","       [0.50070066, 0.49929934],\n","       [0.50174149, 0.49825851],\n","       [0.50190098, 0.49809902],\n","       [0.5007135 , 0.4992865 ],\n","       [0.50207309, 0.49792691],\n","       [0.50270744, 0.49729256],\n","       [0.50050176, 0.49949824],\n","       [0.50142338, 0.49857662],\n","       [0.50099023, 0.49900977],\n","       [0.50084396, 0.49915604],\n","       [0.50368091, 0.49631909],\n","       [0.50272862, 0.49727138],\n","       [0.50132287, 0.49867713],\n","       [0.50401085, 0.49598915],\n","       [0.50227543, 0.49772457],\n","       [0.50117773, 0.49882227],\n","       [0.50165111, 0.49834889],\n","       [0.49957088, 0.50042912],\n","       [0.50317179, 0.49682821],\n","       [0.50098188, 0.49901812],\n","       [0.5023283 , 0.4976717 ],\n","       [0.50104249, 0.49895751],\n","       [0.50104179, 0.49895821],\n","       [0.501309  , 0.498691  ],\n","       [0.50216322, 0.49783678],\n","       [0.50110205, 0.49889795],\n","       [0.50257045, 0.49742955],\n","       [0.5013388 , 0.4986612 ],\n","       [0.50053838, 0.49946162],\n","       [0.50120029, 0.49879971],\n","       [0.50127632, 0.49872368],\n","       [0.50183619, 0.49816381],\n","       [0.50009396, 0.49990604],\n","       [0.50128105, 0.49871895],\n","       [0.50158456, 0.49841544],\n","       [0.50190347, 0.49809653],\n","       [0.50228193, 0.49771807],\n","       [0.50242186, 0.49757814],\n","       [0.50201385, 0.49798615],\n","       [0.50201075, 0.49798925],\n","       [0.5007775 , 0.4992225 ],\n","       [0.50240731, 0.49759269],\n","       [0.49865226, 0.50134774],\n","       [0.50451121, 0.49548879],\n","       [0.50254794, 0.49745206],\n","       [0.5039106 , 0.4960894 ],\n","       [0.50200714, 0.49799286],\n","       [0.50227154, 0.49772846],\n","       [0.50115683, 0.49884317],\n","       [0.50254911, 0.49745089],\n","       [0.50062667, 0.49937333],\n","       [0.50324846, 0.49675154],\n","       [0.50165038, 0.49834962],\n","       [0.50200129, 0.49799871],\n","       [0.50034533, 0.49965467],\n","       [0.50051902, 0.49948098],\n","       [0.50029115, 0.49970885],\n","       [0.50209651, 0.49790349],\n","       [0.50240236, 0.49759764],\n","       [0.50092696, 0.49907304],\n","       [0.50250997, 0.49749003],\n","       [0.4985069 , 0.5014931 ],\n","       [0.4994373 , 0.5005627 ],\n","       [0.5024844 , 0.4975156 ],\n","       [0.50120384, 0.49879616],\n","       [0.49970062, 0.50029938],\n","       [0.50154039, 0.49845961],\n","       [0.50257601, 0.49742399],\n","       [0.5015003 , 0.4984997 ],\n","       [0.50034533, 0.49965467],\n","       [0.5020367 , 0.4979633 ],\n","       [0.5022038 , 0.4977962 ],\n","       [0.50057621, 0.49942379],\n","       [0.50128065, 0.49871935],\n","       [0.50042842, 0.49957158],\n","       [0.50330239, 0.49669761],\n","       [0.50128544, 0.49871456],\n","       [0.50196417, 0.49803583],\n","       [0.50194098, 0.49805902],\n","       [0.50115364, 0.49884636],\n","       [0.50299407, 0.49700593],\n","       [0.50130317, 0.49869683],\n","       [0.50375311, 0.49624689],\n","       [0.50121088, 0.49878912],\n","       [0.50232441, 0.49767559],\n","       [0.50272548, 0.49727452],\n","       [0.50001357, 0.49998643],\n","       [0.50093936, 0.49906064],\n","       [0.50208529, 0.49791471],\n","       [0.50137482, 0.49862518],\n","       [0.50080493, 0.49919507],\n","       [0.50332574, 0.49667426],\n","       [0.50302616, 0.49697384],\n","       [0.50196237, 0.49803763],\n","       [0.5023057 , 0.4976943 ],\n","       [0.5006412 , 0.4993588 ],\n","       [0.50224065, 0.49775935],\n","       [0.5010883 , 0.4989117 ],\n","       [0.50133566, 0.49866434],\n","       [0.50233576, 0.49766424],\n","       [0.50184149, 0.49815851],\n","       [0.50098641, 0.49901359],\n","       [0.5019006 , 0.4980994 ],\n","       [0.50107183, 0.49892817],\n","       [0.50143271, 0.49856729],\n","       [0.50203595, 0.49796405],\n","       [0.5026515 , 0.4973485 ],\n","       [0.50377703, 0.49622297],\n","       [0.49893439, 0.50106561],\n","       [0.50339275, 0.49660725],\n","       [0.50152766, 0.49847234],\n","       [0.50158634, 0.49841366],\n","       [0.50000096, 0.49999904],\n","       [0.50045491, 0.49954509],\n","       [0.50208306, 0.49791694],\n","       [0.50352855, 0.49647145],\n","       [0.50237312, 0.49762688],\n","       [0.49928464, 0.50071536],\n","       [0.50297955, 0.49702045],\n","       [0.50170373, 0.49829627],\n","       [0.50143102, 0.49856898],\n","       [0.50330941, 0.49669059],\n","       [0.50211473, 0.49788527],\n","       [0.50202076, 0.49797924],\n","       [0.50051225, 0.49948775],\n","       [0.50112964, 0.49887036],\n","       [0.50185392, 0.49814608],\n","       [0.49966058, 0.50033942],\n","       [0.50075293, 0.49924707],\n","       [0.50071296, 0.49928704],\n","       [0.49923395, 0.50076605],\n","       [0.50080148, 0.49919852],\n","       [0.49827919, 0.50172081],\n","       [0.50168248, 0.49831752],\n","       [0.49995601, 0.50004399],\n","       [0.5014818 , 0.4985182 ],\n","       [0.5008324 , 0.4991676 ],\n","       [0.50225201, 0.49774799],\n","       [0.50041346, 0.49958654],\n","       [0.50008176, 0.49991824],\n","       [0.50275251, 0.49724749],\n","       [0.5013088 , 0.4986912 ],\n","       [0.50199637, 0.49800363],\n","       [0.50250964, 0.49749036],\n","       [0.50279177, 0.49720823],\n","       [0.50085703, 0.49914297],\n","       [0.50363226, 0.49636774],\n","       [0.50075518, 0.49924482],\n","       [0.50160237, 0.49839763],\n","       [0.50200633, 0.49799367],\n","       [0.50294549, 0.49705451],\n","       [0.50134492, 0.49865508],\n","       [0.50113355, 0.49886645],\n","       [0.5011625 , 0.4988375 ],\n","       [0.50086075, 0.49913925],\n","       [0.50223575, 0.49776425],\n","       [0.50032738, 0.49967262],\n","       [0.50110359, 0.49889641],\n","       [0.50256978, 0.49743022],\n","       [0.49850301, 0.50149699],\n","       [0.5031758 , 0.4968242 ],\n","       [0.50058575, 0.49941425],\n","       [0.5004442 , 0.4995558 ],\n","       [0.50093672, 0.49906328],\n","       [0.50332508, 0.49667492],\n","       [0.50399354, 0.49600646],\n","       [0.50351518, 0.49648482],\n","       [0.50156107, 0.49843893],\n","       [0.50078711, 0.49921289],\n","       [0.50197128, 0.49802872]])"]},"metadata":{"tags":[]},"execution_count":19}]},{"metadata":{"id":"OQbMGan0f_9v","colab_type":"code","outputId":"fb915271-4ae9-454d-dd1b-87cd4619f6a0","executionInfo":{"status":"ok","timestamp":1547604372204,"user_tz":-480,"elapsed":661,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["mnb.score(Xtest_,Ytest)"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.5433333333333333"]},"metadata":{"tags":[]},"execution_count":20}]},{"metadata":{"id":"Mc4khe9Kf_9w","colab_type":"code","outputId":"e76c9a51-54ff-45c5-9b6c-6ae0beb6a43d","executionInfo":{"status":"ok","timestamp":1547604375227,"user_tz":-480,"elapsed":787,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["brier_score_loss(Ytest,mnb.predict_proba(Xtest_)[:,1],pos_label=1)"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.24977828412546027"]},"metadata":{"tags":[]},"execution_count":21}]},{"metadata":{"id":"ZbdwHxh-f_9x","colab_type":"code","colab":{}},"cell_type":"code","source":["#原数据是连续性数据\n","#来试试看把Xtiain转换成分类型数据吧\n","#注意我们的Xtrain没有经过归一化，因为做哑变量之后自然所有的数据就不会又负数了\n","from sklearn.preprocessing import KBinsDiscretizer #对连续性变量进行分箱\n","kbs = KBinsDiscretizer(n_bins=10, encode='onehot').fit(Xtrain)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"1vnlIqAqf_9z","colab_type":"code","colab":{}},"cell_type":"code","source":["Xtrain_ = kbs.transform(Xtrain)\n","Xtest_ = kbs.transform(Xtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"SVfaXS8Df_9z","colab_type":"code","outputId":"45662aa2-0a14-4cb6-9a88-d01c7181eefb","executionInfo":{"status":"ok","timestamp":1547604604867,"user_tz":-480,"elapsed":886,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["Xtrain_.shape #2个特征中，每个特征分了10个箱所分出来的哑变量"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(700, 20)"]},"metadata":{"tags":[]},"execution_count":24}]},{"metadata":{"id":"gbQkQvqBf_90","colab_type":"code","colab":{}},"cell_type":"code","source":["mnb = MultinomialNB().fit(Xtrain_, Ytrain)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"0hSjq08xf_92","colab_type":"code","outputId":"1c3f1255-1459-4a8a-f664-6ee373952a62","executionInfo":{"status":"ok","timestamp":1547604637893,"user_tz":-480,"elapsed":770,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["mnb.score(Xtest_,Ytest)"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9966666666666667"]},"metadata":{"tags":[]},"execution_count":26}]},{"metadata":{"id":"8boThgNMf_96","colab_type":"code","outputId":"a6c2dd77-558e-4ae8-c894-fd211d705894","executionInfo":{"status":"ok","timestamp":1547604639781,"user_tz":-480,"elapsed":713,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["brier_score_loss(Ytest,mnb.predict_proba(Xtest_)[:,1],pos_label=1)"],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.0014593932778211862"]},"metadata":{"tags":[]},"execution_count":27}]},{"metadata":{"id":"WrJElzeNnG1Q","colab_type":"text"},"cell_type":"markdown","source":["## 伯努利朴素贝叶斯BernoulliNB"]},{"metadata":{"id":"EVZdNiBfbX46","colab_type":"text"},"cell_type":"markdown","source":["二项分布"]},{"metadata":{"id":"MjtLbcMtf_96","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.naive_bayes import BernoulliNB"],"execution_count":0,"outputs":[]},{"metadata":{"id":"K1cmnYtBf_97","colab_type":"code","colab":{}},"cell_type":"code","source":["#普通来说我们应该使用二值化的类sklearn.preprocessing.Binarizer来将特征一个个二值化\n","#然而这样效率过低，因此我们选择归一化之后直接设置一个阈值\n","\n","mms = MinMaxScaler().fit(Xtrain)\n","Xtrain_ = mms.transform(Xtrain)\n","Xtest_ = mms.transform(Xtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"A6rG3iPVf_98","colab_type":"code","outputId":"9256d42c-5d06-4153-9ea9-f52fdbfc7381","executionInfo":{"status":"ok","timestamp":1547605085201,"user_tz":-480,"elapsed":676,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["#不设置二值化\n","bnl_ = BernoulliNB().fit(Xtrain_, Ytrain)\n","bnl_.score(Xtest_,Ytest)"],"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.49666666666666665"]},"metadata":{"tags":[]},"execution_count":30}]},{"metadata":{"id":"0t4BetElf_99","colab_type":"code","outputId":"cff07e82-d6fe-4021-c92b-d8ee7c6b8171","executionInfo":{"status":"ok","timestamp":1547605088159,"user_tz":-480,"elapsed":686,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["brier_score_loss(Ytest,bnl_.predict_proba(Xtest_)[:,1],pos_label=1)"],"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.25000009482193225"]},"metadata":{"tags":[]},"execution_count":31}]},{"metadata":{"id":"pQ4VVvr8f_-A","colab_type":"code","outputId":"29646318-8c6b-428b-9f39-62ecfb689dec","executionInfo":{"status":"ok","timestamp":1547605174114,"user_tz":-480,"elapsed":740,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["#设置二值化阈值为0.5\n","bnl = BernoulliNB(binarize=0.5).fit(Xtrain_, Ytrain)\n","bnl.score(Xtest_,Ytest)"],"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9833333333333333"]},"metadata":{"tags":[]},"execution_count":32}]},{"metadata":{"id":"rOq0te7Ff_-C","colab_type":"code","outputId":"8b226c1b-04f7-48c7-a84c-8f07c6921595","executionInfo":{"status":"ok","timestamp":1547605176143,"user_tz":-480,"elapsed":681,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["brier_score_loss(Ytest,bnl.predict_proba(Xtest_)[:,1],pos_label=1)"],"execution_count":33,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.010405875827339534"]},"metadata":{"tags":[]},"execution_count":33}]},{"metadata":{"id":"wALvwiawnOpA","colab_type":"text"},"cell_type":"markdown","source":["## 探索贝叶斯：贝叶斯的样本不均衡问题"]},{"metadata":{"id":"Qoy9g51Af_-E","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.naive_bayes import MultinomialNB, GaussianNB, BernoulliNB\n","from sklearn.model_selection import train_test_split\n","from sklearn.datasets import make_blobs \n","from sklearn.preprocessing import KBinsDiscretizer\n","from sklearn.metrics import brier_score_loss as BS,recall_score,roc_auc_score as AUC"],"execution_count":0,"outputs":[]},{"metadata":{"id":"dOt5qXkOf_-F","colab_type":"code","colab":{}},"cell_type":"code","source":["class_1 = 50000 #多数类为50000个样本\n","class_2 = 500 #少数类为500个样本 1%\n","centers = [[0.0, 0.0], [5.0, 5.0]] #设定两个类别的中心\n","clusters_std = [3, 1] #设定两个类别的方差\n","X, y = make_blobs(n_samples=[class_1, class_2],\n","                  centers=centers,\n","                  cluster_std=clusters_std,\n","                  random_state=0, shuffle=False)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"aq7uyKbZf_-G","colab_type":"code","outputId":"ed6f1210-4aab-4c31-9f9e-adcf2c093687","executionInfo":{"status":"ok","timestamp":1547605543374,"user_tz":-480,"elapsed":671,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["X.shape"],"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(50500, 2)"]},"metadata":{"tags":[]},"execution_count":36}]},{"metadata":{"id":"0poWklLRf_-H","colab_type":"code","outputId":"7d6ce3c0-646f-4d36-c670-9e636b21f382","executionInfo":{"status":"ok","timestamp":1547605545554,"user_tz":-480,"elapsed":663,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["np.unique(y)"],"execution_count":37,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1])"]},"metadata":{"tags":[]},"execution_count":37}]},{"metadata":{"id":"j0jkDseYf_-I","colab_type":"code","colab":{}},"cell_type":"code","source":["name = [\"Multinomial\",\"Gaussian\",\"Bernoulli\"]\n","models = [MultinomialNB(),GaussianNB(),BernoulliNB()]"],"execution_count":0,"outputs":[]},{"metadata":{"id":"0s7uvABmf_-J","colab_type":"code","outputId":"9dde9e7a-edaa-4a00-877a-a4e8477a23f2","executionInfo":{"status":"ok","timestamp":1547540708043,"user_tz":-480,"elapsed":818,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":272}},"cell_type":"code","source":["for name,clf in zip(name,models):\n","    #分测试集和训练集\n","    Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y\n","                                                ,test_size=0.3\n","                                                ,random_state=420)\n","    #预处理\n","    if name != \"Gaussian\":\n","        kbs = KBinsDiscretizer(n_bins=10, encode='onehot').fit(Xtrain)\n","        Xtrain = kbs.transform(Xtrain)\n","        Xtest = kbs.transform(Xtest)\n","    \n","    #拟合&结果\n","    clf.fit(Xtrain,Ytrain)\n","    y_pred = clf.predict(Xtest)\n","    proba = clf.predict_proba(Xtest)[:,1]\n","    score = clf.score(Xtest,Ytest) #准确率\n","    print(name)\n","    print(\"\\tBrier:{:.3f}\".format(BS(Ytest,proba,pos_label=1)))\n","    print(\"\\tAccuracy:{:.3f}\".format(score))\n","    print(\"\\tRecall:{:.3f}\".format(recall_score(Ytest,y_pred)))\n","    print(\"\\tAUC:{:.3f}\".format(AUC(Ytest,proba)))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Multinomial\n","\tBrier:0.007\n","\tAccuracy:0.990\n","\tRecall:0.000\n","\tAUC:0.991\n","Gaussian\n","\tBrier:0.006\n","\tAccuracy:0.990\n","\tRecall:0.438\n","\tAUC:0.993\n","Bernoulli\n","\tBrier:0.009\n","\tAccuracy:0.987\n","\tRecall:0.771\n","\tAUC:0.987\n"],"name":"stdout"}]},{"metadata":{"id":"KxBEr7xHf6CO","colab_type":"text"},"cell_type":"markdown","source":["少数类的效果比较差"]},{"metadata":{"id":"u8NmzlWWnX85","colab_type":"text"},"cell_type":"markdown","source":["## 改进多项式朴素贝叶斯：补集朴素贝叶斯ComplementNB"]},{"metadata":{"id":"2qrNoJQGf_-L","colab_type":"code","outputId":"6c44f564-8803-432f-954d-bd0b939b912d","executionInfo":{"status":"ok","timestamp":1547607482942,"user_tz":-480,"elapsed":770,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":425}},"cell_type":"code","source":["from sklearn.naive_bayes import ComplementNB\n","from time import time\n","import datetime\n","\n","name = [\"Multinomial\",\"Gaussian\",\"Bernoulli\",\"Complement\"]\n","models = [MultinomialNB(),GaussianNB(),BernoulliNB(),ComplementNB()]\n","\n","for name,clf in zip(name,models):\n","    times = time()\n","    Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y\n","                                                ,test_size=0.3\n","                                                ,random_state=420)\n","    #预处理\n","    if name != \"Gaussian\":\n","        kbs = KBinsDiscretizer(n_bins=10, encode='onehot').fit(Xtrain)\n","        Xtrain = kbs.transform(Xtrain)\n","        Xtest = kbs.transform(Xtest)\n","    \n","    clf.fit(Xtrain,Ytrain)\n","    y_pred = clf.predict(Xtest)\n","    proba = clf.predict_proba(Xtest)[:,1]\n","    score = clf.score(Xtest,Ytest)\n","    print(name)\n","    print(\"\\tBrier:{:.3f}\".format(BS(Ytest,proba,pos_label=1)))\n","    print(\"\\tAccuracy:{:.3f}\".format(score))\n","    print(\"\\tRecall:{:.3f}\".format(recall_score(Ytest,y_pred)))\n","    print(\"\\tAUC:{:.3f}\".format(AUC(Ytest,proba)))\n","    print(datetime.datetime.fromtimestamp(time()-times).strftime(\"%M:%S:%f\"))"],"execution_count":39,"outputs":[{"output_type":"stream","text":["Multinomial\n","\tBrier:0.007\n","\tAccuracy:0.990\n","\tRecall:0.000\n","\tAUC:0.991\n","00:00:056550\n","Gaussian\n","\tBrier:0.006\n","\tAccuracy:0.990\n","\tRecall:0.438\n","\tAUC:0.993\n","00:00:025651\n","Bernoulli\n","\tBrier:0.009\n","\tAccuracy:0.987\n","\tRecall:0.771\n","\tAUC:0.987\n","00:00:048315\n","Complement\n","\tBrier:0.038\n","\tAccuracy:0.953\n","\tRecall:0.987\n","\tAUC:0.991\n","00:00:045882\n"],"name":"stdout"}]},{"metadata":{"id":"DfvOP_1OniLR","colab_type":"text"},"cell_type":"markdown","source":["# 案例：贝叶斯分类器做文本分类"]},{"metadata":{"id":"YLz1IpD5obQi","colab_type":"text"},"cell_type":"markdown","source":["## 文本编码技术简介"]},{"metadata":{"id":"o7PfiT8JofTH","colab_type":"text"},"cell_type":"markdown","source":["单词计数向量"]},{"metadata":{"id":"zMeTcI1cf_-M","colab_type":"code","colab":{}},"cell_type":"code","source":["sample = [\"Machine learning is fascinating, it is wonderful\"\n","          ,\"Machine learning is a sensational techonology\"\n","          ,\"Elsa is a popular character\"]"],"execution_count":0,"outputs":[]},{"metadata":{"id":"XPIoVVxaf_-N","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.feature_extraction.text import CountVectorizer"],"execution_count":0,"outputs":[]},{"metadata":{"id":"SjaAeipvf_-O","colab_type":"code","colab":{}},"cell_type":"code","source":["vec = CountVectorizer()"],"execution_count":0,"outputs":[]},{"metadata":{"id":"wUSAZbIFf_-Q","colab_type":"code","colab":{}},"cell_type":"code","source":["X = vec.fit_transform(sample)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"rAyxh6i-f_-Q","colab_type":"code","outputId":"d8ad0354-a2c4-4826-8e27-731dedba1365","executionInfo":{"status":"ok","timestamp":1547608330459,"user_tz":-480,"elapsed":733,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"cell_type":"code","source":["X"],"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<3x11 sparse matrix of type '<class 'numpy.int64'>'\n","\twith 15 stored elements in Compressed Sparse Row format>"]},"metadata":{"tags":[]},"execution_count":44}]},{"metadata":{"id":"tXos6fdUf_-R","colab_type":"code","outputId":"95644d16-0200-45b2-a7c7-e667bcb37bd1","executionInfo":{"status":"ok","timestamp":1547608332824,"user_tz":-480,"elapsed":665,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"cell_type":"code","source":["#使用接口get_feature_names()调用每个列的名称\n","\n","vec.get_feature_names() #按照字母的顺序排列"],"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['character',\n"," 'elsa',\n"," 'fascinating',\n"," 'is',\n"," 'it',\n"," 'learning',\n"," 'machine',\n"," 'popular',\n"," 'sensational',\n"," 'techonology',\n"," 'wonderful']"]},"metadata":{"tags":[]},"execution_count":45}]},{"metadata":{"id":"D8a5aCGIf_-S","colab_type":"code","colab":{}},"cell_type":"code","source":["import pandas as pd"],"execution_count":0,"outputs":[]},{"metadata":{"id":"ZbRkGHr4f_-T","colab_type":"code","colab":{}},"cell_type":"code","source":["#注意稀疏矩阵是无法输入pandas的\n","CVresult = pd.DataFrame(X.toarray(),columns = vec.get_feature_names())"],"execution_count":0,"outputs":[]},{"metadata":{"id":"_l4HaryWf_-U","colab_type":"code","outputId":"fc987254-4600-483f-9696-7410e70f8d82","executionInfo":{"status":"ok","timestamp":1547608343580,"user_tz":-480,"elapsed":749,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":162}},"cell_type":"code","source":["CVresult"],"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>character</th>\n","      <th>elsa</th>\n","      <th>fascinating</th>\n","      <th>is</th>\n","      <th>it</th>\n","      <th>learning</th>\n","      <th>machine</th>\n","      <th>popular</th>\n","      <th>sensational</th>\n","      <th>techonology</th>\n","      <th>wonderful</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   character  elsa  fascinating  is  it  learning  machine  popular  \\\n","0          0     0            1   2   1         1        1        0   \n","1          0     0            0   1   0         1        1        0   \n","2          1     1            0   1   0         0        0        1   \n","\n","   sensational  techonology  wonderful  \n","0            0            0          1  \n","1            1            1          0  \n","2            0            0          0  "]},"metadata":{"tags":[]},"execution_count":48}]},{"metadata":{"id":"VOFvKddzf_-W","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.feature_extraction.text import TfidfVectorizer as TFIDF\n","vec = TFIDF()"],"execution_count":0,"outputs":[]},{"metadata":{"id":"9jIpPDlXf_-a","colab_type":"code","colab":{}},"cell_type":"code","source":["X = vec.fit_transform(sample)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"0o_bdINif_-b","colab_type":"code","outputId":"ce5e8390-a7c7-477f-abac-1baeac47aa07","executionInfo":{"status":"ok","timestamp":1547613958536,"user_tz":-480,"elapsed":733,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"cell_type":"code","source":["X #每一个单词作为一个特征，每个单词在这个句子中所占的比例"],"execution_count":51,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<3x11 sparse matrix of type '<class 'numpy.float64'>'\n","\twith 15 stored elements in Compressed Sparse Row format>"]},"metadata":{"tags":[]},"execution_count":51}]},{"metadata":{"id":"nVMM6851f_-d","colab_type":"code","colab":{}},"cell_type":"code","source":["#同样使用接口get_feature_names()调用每个列的名称\n","TFIDFresult = pd.DataFrame(X.toarray(),columns=vec.get_feature_names())"],"execution_count":0,"outputs":[]},{"metadata":{"id":"_a_ubWqVf_-e","colab_type":"code","outputId":"cae8a7b9-3519-4238-8c3d-9d3e97e5ae11","executionInfo":{"status":"ok","timestamp":1547613973969,"user_tz":-480,"elapsed":669,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":162}},"cell_type":"code","source":["TFIDFresult"],"execution_count":53,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>character</th>\n","      <th>elsa</th>\n","      <th>fascinating</th>\n","      <th>is</th>\n","      <th>it</th>\n","      <th>learning</th>\n","      <th>machine</th>\n","      <th>popular</th>\n","      <th>sensational</th>\n","      <th>techonology</th>\n","      <th>wonderful</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.424396</td>\n","      <td>0.501310</td>\n","      <td>0.424396</td>\n","      <td>0.322764</td>\n","      <td>0.322764</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.424396</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.315444</td>\n","      <td>0.000000</td>\n","      <td>0.406192</td>\n","      <td>0.406192</td>\n","      <td>0.000000</td>\n","      <td>0.534093</td>\n","      <td>0.534093</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.546454</td>\n","      <td>0.546454</td>\n","      <td>0.000000</td>\n","      <td>0.322745</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.546454</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   character      elsa  fascinating        is        it  learning   machine  \\\n","0   0.000000  0.000000     0.424396  0.501310  0.424396  0.322764  0.322764   \n","1   0.000000  0.000000     0.000000  0.315444  0.000000  0.406192  0.406192   \n","2   0.546454  0.546454     0.000000  0.322745  0.000000  0.000000  0.000000   \n","\n","    popular  sensational  techonology  wonderful  \n","0  0.000000     0.000000     0.000000   0.424396  \n","1  0.000000     0.534093     0.534093   0.000000  \n","2  0.546454     0.000000     0.000000   0.000000  "]},"metadata":{"tags":[]},"execution_count":53}]},{"metadata":{"id":"VOBKdtgzf_-e","colab_type":"code","colab":{}},"cell_type":"code","source":["#使用TF-IDF编码之后，出现得多的单词的权重被降低了么？ theta"],"execution_count":0,"outputs":[]},{"metadata":{"id":"_x2gwqaOf_-f","colab_type":"code","outputId":"61b665df-4f04-4be0-d386-23102f4c9ea3","executionInfo":{"status":"ok","timestamp":1547614098138,"user_tz":-480,"elapsed":660,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":221}},"cell_type":"code","source":["CVresult.sum(axis=0)"],"execution_count":54,"outputs":[{"output_type":"execute_result","data":{"text/plain":["character      1\n","elsa           1\n","fascinating    1\n","is             4\n","it             1\n","learning       2\n","machine        2\n","popular        1\n","sensational    1\n","techonology    1\n","wonderful      1\n","dtype: int64"]},"metadata":{"tags":[]},"execution_count":54}]},{"metadata":{"id":"ZN09fnv-f_-h","colab_type":"code","outputId":"ab4faab5-03b5-4a2e-ab65-ffc5c7ba3cab","executionInfo":{"status":"ok","timestamp":1547540825009,"user_tz":-480,"elapsed":959,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":221}},"cell_type":"code","source":["CVresult.sum(axis=0)/CVresult.sum(axis=0).sum()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["character      0.0625\n","elsa           0.0625\n","fascinating    0.0625\n","is             0.2500\n","it             0.0625\n","learning       0.1250\n","machine        0.1250\n","popular        0.0625\n","sensational    0.0625\n","techonology    0.0625\n","wonderful      0.0625\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":157}]},{"metadata":{"id":"DkBHtSiFf_-k","colab_type":"code","outputId":"307986e3-9840-47e6-d475-582dea3334b4","executionInfo":{"status":"ok","timestamp":1547540828410,"user_tz":-480,"elapsed":978,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":221}},"cell_type":"code","source":["TFIDFresult.sum(axis=0) / TFIDFresult.sum(axis=0).sum()\n","#将原本出现次数比较多的词压缩我们的权重\n","#将原本出现次数比较少的词增加我们的权重"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["character      0.083071\n","elsa           0.083071\n","fascinating    0.064516\n","is             0.173225\n","it             0.064516\n","learning       0.110815\n","machine        0.110815\n","popular        0.083071\n","sensational    0.081192\n","techonology    0.081192\n","wonderful      0.064516\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":158}]},{"metadata":{"id":"7UMzdkdwn0P2","colab_type":"text"},"cell_type":"markdown","source":["## 探索文本数据"]},{"metadata":{"id":"9KbNvuWUf_-l","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.datasets import fetch_20newsgroups"],"execution_count":0,"outputs":[]},{"metadata":{"id":"QFASO0Djf_-n","colab_type":"code","outputId":"d13842a3-7d19-4786-c938-c08bbd0d3ec4","executionInfo":{"status":"ok","timestamp":1547614479943,"user_tz":-480,"elapsed":16893,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"cell_type":"code","source":["#初次使用这个数据集的时候，会在实例化的时候开始下载\n","data = fetch_20newsgroups()"],"execution_count":56,"outputs":[{"output_type":"stream","text":["Downloading 20news dataset. This may take a few minutes.\n","Downloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)\n"],"name":"stderr"}]},{"metadata":{"id":"uoI2LvoHf_-o","colab_type":"code","colab":{}},"cell_type":"code","source":["#通常我们使用data来查看data里面到底包含了什么内容\n","#但由于fetch_20newsgourps这个类加载出的数据巨大，数据结构中混杂很多文字，因此很难去看清"],"execution_count":0,"outputs":[]},{"metadata":{"id":"SqoIlvzrf_-o","colab_type":"code","outputId":"ec574d42-0a53-4b8f-8589-537d6ce7c081","executionInfo":{"status":"ok","timestamp":1547615228817,"user_tz":-480,"elapsed":1489,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":357}},"cell_type":"code","source":["#不同类型的新闻\n","#标签的分类都有哪些\n","data.target_names"],"execution_count":58,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['alt.atheism',\n"," 'comp.graphics',\n"," 'comp.os.ms-windows.misc',\n"," 'comp.sys.ibm.pc.hardware',\n"," 'comp.sys.mac.hardware',\n"," 'comp.windows.x',\n"," 'misc.forsale',\n"," 'rec.autos',\n"," 'rec.motorcycles',\n"," 'rec.sport.baseball',\n"," 'rec.sport.hockey',\n"," 'sci.crypt',\n"," 'sci.electronics',\n"," 'sci.med',\n"," 'sci.space',\n"," 'soc.religion.christian',\n"," 'talk.politics.guns',\n"," 'talk.politics.mideast',\n"," 'talk.politics.misc',\n"," 'talk.religion.misc']"]},"metadata":{"tags":[]},"execution_count":58}]},{"metadata":{"id":"xe0-KtsHf_-q","colab_type":"code","colab":{}},"cell_type":"code","source":["#其实fetch_20newsgroups也是一个类，既然是类，应该就有可以调用的参数\n","#面对简单数据集，我们往往在实例化的过程中什么都不写，但是现在data中数据量太多，不方便探索\n","#因此我们需要来看看我们的类fetch_20newsgroups都有什么样的参数可以帮助我们"],"execution_count":0,"outputs":[]},{"metadata":{"id":"BjHYvLKff_-q","colab_type":"code","colab":{}},"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","\n","categories = [\"sci.space\" #科学技术 - 太空\n","              ,\"rec.sport.hockey\" #运动 - 曲棍球\n","              ,\"talk.politics.guns\" #政治 - 枪支问题\n","              ,\"talk.politics.mideast\"] #政治 - 中东问题\n","\n","train = fetch_20newsgroups(subset=\"train\",categories = categories)\n","test = fetch_20newsgroups(subset=\"test\",categories = categories)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"zJAqS4rwf_-r","colab_type":"code","outputId":"91231e4d-e3e8-4810-9f3a-94017cd0b302","executionInfo":{"status":"ok","timestamp":1547540880706,"user_tz":-480,"elapsed":2379,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":17292,"output_embedded_package_id":"1dIm8_dPdGYEXAZIOYVjGG_UdTEaTE0QM"}},"cell_type":"code","source":["train\n","#可以观察到，里面依然是类字典结构，我们可以通过使用键的方式来提取内容"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"metadata":{"id":"UvATyowQf_-u","colab_type":"code","outputId":"b3f4f67d-5b1c-43a0-c94e-5b2a1fdd8db2","executionInfo":{"status":"ok","timestamp":1547615254475,"user_tz":-480,"elapsed":731,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"cell_type":"code","source":["train.target_names #四个类别，四个目录下的标签的分类"],"execution_count":60,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['rec.sport.hockey',\n"," 'sci.space',\n"," 'talk.politics.guns',\n"," 'talk.politics.mideast']"]},"metadata":{"tags":[]},"execution_count":60}]},{"metadata":{"id":"holD4je8f_-v","colab_type":"code","outputId":"3e64b0eb-3ab6-47a5-87dc-e6aac1cd6aac","executionInfo":{"status":"ok","timestamp":1547615261404,"user_tz":-480,"elapsed":669,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["#查看总共有多少篇文章存在\n","len(train.data)"],"execution_count":61,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2303"]},"metadata":{"tags":[]},"execution_count":61}]},{"metadata":{"id":"Yl8xWagcf_-x","colab_type":"code","outputId":"40640ebf-1ab2-4c20-f0e4-6f2b0d1bf56c","executionInfo":{"status":"ok","timestamp":1547540896243,"user_tz":-480,"elapsed":807,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":1003}},"cell_type":"code","source":["#随意提取一篇文章来看看\n","print(train.data[0])"],"execution_count":0,"outputs":[{"output_type":"stream","text":["From: tvartiai@vipunen.hut.fi (Tommi Vartiainen)\n","Subject: Re: Finland/Sweden vs.NHL teams (WAS:Helsinki/Stockholm & NHL expansion)\n","Nntp-Posting-Host: vipunen.hut.fi\n","Organization: Helsinki University of Technology, Finland\n","Lines: 51\n","\n","In <1993Apr16.195754.5476@ousrvr.oulu.fi> mep@phoenix.oulu.fi (Marko Poutiainen) writes:\n","\n",">: FINLAND:  \n",">: \n",">: D-Jyrki Lumme.......20\n",">: D-Teppo Numminen....20\n",">: D-Peter Ahola.......13\n",">: \n",">Well well, they don't like our defenders (mainly Lumme and Numminen)...\n","\n","About 25 is correct for Numminen and Lumme.\n","\n","\n",">: R-Teemu Selanne.....27\n",">: \n",">Compared to Kurri, Selanne's points are too high, lets make it 25 or 26.\n","\n","No, Kurri's points are too low. 27 for Kurri and 28 for Sel{nne.\n","\n",">: well in the Canada Cup and World Championships largely due to the efforts of\n",">: Markus Ketterer (the goalie), 3-4 or the players listed above and luck. There's\n",">: presumably a lot of decent players in Finland that wouldn't be superstars at\n",">: the highest level but still valuable role players, however. My guess would be\n",">: that the Finnish Canada Cup team would be a .500 team in the NHL.\n","\n",">Wow, now, it looks like you don't like our players? What about guys like:\n",">Nieminen, Jutila, Riihijarvi, Varvio, Laukkanen, Makela, Keskinen and (even\n",">if he is aging) Ruotsalainen? The main difference between finnish and North-\n",">American players is, that our players tend to be better in the larger rink.\n",">The Canadian defenders are usually slower that defenders in Europe. \n",">And I think that there was more in our success than Ketterer and luck (though\n",">they helped). I think that the main reason was, that the team worked well\n",">together.\n","\n","\n","That's true. Game is so different here in Europe compared to NHL. North-ame-\n","ricans are better in small rinks and europeans in large rinks. An average\n","european player from Sweden, Finland, Russian or Tsech/Slovakia is a better \n","skater and  puckhandler than his NHL colleague. Especially defenders in NHL\n","are mainly slow and clumsy. Sel{nne has also said that in the Finnish Sm-league\n","game is more based on skill than in NHL. In Finland he couldn't get so many \n","breakaways because defenders here are an average much better skaters than in\n","NHL. Also Alpo Suhonen said that in NHL Sel{nne's speed accentuates because\n","of clumsy defensemen.\n","\n","I have to admit that the best players come from Canada, but those regulars\n","aren't as skilful as regulars in the best european leagues. Also top europeans\n","are in the same level as the best north-americans.(except Lemieux is in the\n","class of his own). \n","\n","Tommi\n","\n"],"name":"stdout"}]},{"metadata":{"id":"B8R13fyef_-y","colab_type":"code","outputId":"bdde0fac-601e-4f11-b656-ffe99ba302fd","executionInfo":{"status":"ok","timestamp":1547615384295,"user_tz":-480,"elapsed":706,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["#查看一下我们的标签\n","np.unique(train.target)"],"execution_count":62,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1, 2, 3])"]},"metadata":{"tags":[]},"execution_count":62}]},{"metadata":{"id":"o5AAoMPgf_-y","colab_type":"code","outputId":"bb29a42a-4e58-430b-f8bb-44640dbe51f4","executionInfo":{"status":"ok","timestamp":1547615412715,"user_tz":-480,"elapsed":702,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["len(train.target)"],"execution_count":63,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2303"]},"metadata":{"tags":[]},"execution_count":63}]},{"metadata":{"id":"8IVmOPi0f_-z","colab_type":"code","outputId":"467b8bea-1c77-455d-9c36-9f89165db2af","executionInfo":{"status":"ok","timestamp":1547615427571,"user_tz":-480,"elapsed":874,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"cell_type":"code","source":["#是否存在样本不平衡问题？\n","for i in [1,2,3]:\n","\tprint(i,(train.target == i).sum()/len(train.target))"],"execution_count":64,"outputs":[{"output_type":"stream","text":["1 0.25749023013460703\n","2 0.23708206686930092\n","3 0.24489795918367346\n"],"name":"stdout"}]},{"metadata":{"id":"EOqQ5r9ooHC5","colab_type":"text"},"cell_type":"markdown","source":["## 使用TF-IDF将文本数据编码"]},{"metadata":{"id":"CtELe4TGf_-z","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.feature_extraction.text import TfidfVectorizer as TFIDF"],"execution_count":0,"outputs":[]},{"metadata":{"id":"mHfwtwfvf_-0","colab_type":"code","colab":{}},"cell_type":"code","source":["Xtrain = train.data\n","Xtest = test.data\n","Ytrain = train.target\n","Ytest = test.target"],"execution_count":0,"outputs":[]},{"metadata":{"id":"ZvoT0b9df_-0","colab_type":"code","colab":{}},"cell_type":"code","source":["tfidf = TFIDF().fit(Xtrain)\n","Xtrain_ = tfidf.transform(Xtrain)\n","Xtest_ = tfidf.transform(Xtest)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"QO5IiIIFf_-1","colab_type":"code","outputId":"3c4c9dbe-1c7c-469b-d937-deba0aead380","executionInfo":{"status":"ok","timestamp":1547615583015,"user_tz":-480,"elapsed":681,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"cell_type":"code","source":["Xtrain_"],"execution_count":68,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<2303x40725 sparse matrix of type '<class 'numpy.float64'>'\n","\twith 430306 stored elements in Compressed Sparse Row format>"]},"metadata":{"tags":[]},"execution_count":68}]},{"metadata":{"id":"Fx94dwSrf_-2","colab_type":"code","colab":{}},"cell_type":"code","source":["tosee = pd.DataFrame(Xtrain_.toarray(),columns=tfidf.get_feature_names())"],"execution_count":0,"outputs":[]},{"metadata":{"id":"oElErqI7f_-4","colab_type":"code","outputId":"3f070144-e4cf-459f-d0d2-d9613e2d2918","executionInfo":{"status":"ok","timestamp":1547615606429,"user_tz":-480,"elapsed":634,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":253}},"cell_type":"code","source":["tosee.head()"],"execution_count":70,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>00</th>\n","   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0.0     0.0  0.0   0.0  0.0  0.0  0.0      0.0   \n","2     0.0   ...     0.0  0.0     0.0  0.0   0.0  0.0  0.0  0.0      0.0   \n","3     0.0   ...     0.0  0.0     0.0  0.0   0.0  0.0  0.0  0.0      0.0   \n","4     0.0   ...     0.0  0.0     0.0  0.0   0.0  0.0  0.0  0.0      0.0   \n","\n","   zzzzzz  \n","0     0.0  \n","1     0.0  \n","2     0.0  \n","3     0.0  \n","4     0.0  \n","\n","[5 rows x 40725 columns]"]},"metadata":{"tags":[]},"execution_count":70}]},{"metadata":{"id":"2Pru2_eNf_-5","colab_type":"code","outputId":"1e7113ab-bef4-46cc-a3fa-054321348ced","executionInfo":{"status":"ok","timestamp":1547540953416,"user_tz":-480,"elapsed":851,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["tosee.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2303, 40725)"]},"metadata":{"tags":[]},"execution_count":176}]},{"metadata":{"id":"2GpkB3jJf_-7","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.naive_bayes import MultinomialNB, ComplementNB, BernoulliNB\n","from sklearn.metrics import brier_score_loss as BS\n","\n","name = [\"Multinomial\",\"Complement\",\"Bournulli\"]\n","#注意高斯朴素贝叶斯不接受稀疏矩阵\n","models = [MultinomialNB(),ComplementNB(),BernoulliNB()]"],"execution_count":0,"outputs":[]},{"metadata":{"id":"YAXLrNv7f_-8","colab_type":"code","outputId":"832aa667-7cc2-4d02-ff3b-48fb6309257a","executionInfo":{"status":"ok","timestamp":1547615874669,"user_tz":-480,"elapsed":706,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":476}},"cell_type":"code","source":["for name,clf in zip(name,models):\n","    clf.fit(Xtrain_,Ytrain)\n","    y_pred = clf.predict(Xtest_)\n","    proba = clf.predict_proba(Xtest_)\n","    score = clf.score(Xtest_,Ytest)\n","    print(name)\n","    \n","    #4个不同的标签取值下的布里尔分数\n","    Bscore = []\n","    for i in range(len(np.unique(Ytrain))):\n","        bs = BS(Ytest,proba[:,i],pos_label=i)\n","        Bscore.append(bs)\n","        print(\"\\tBrier under {}:{:.3f}\".format(train.target_names[i],bs))\n","        \n","    print(\"\\tAverage Brier:{:.3f}\".format(np.mean(Bscore)))\n","    print(\"\\tAccuracy:{:.3f}\".format(score))\n","    print(\"\\n\")"],"execution_count":72,"outputs":[{"output_type":"stream","text":["Multinomial\n","\tBrier under rec.sport.hockey:0.018\n","\tBrier under sci.space:0.033\n","\tBrier under talk.politics.guns:0.030\n","\tBrier under talk.politics.mideast:0.026\n","\tAverage Brier:0.027\n","\tAccuracy:0.975\n","\n","\n","Complement\n","\tBrier under rec.sport.hockey:0.023\n","\tBrier under sci.space:0.039\n","\tBrier under talk.politics.guns:0.039\n","\tBrier under talk.politics.mideast:0.033\n","\tAverage Brier:0.033\n","\tAccuracy:0.986\n","\n","\n","Bournulli\n","\tBrier under rec.sport.hockey:0.068\n","\tBrier under sci.space:0.025\n","\tBrier under talk.politics.guns:0.045\n","\tBrier under talk.politics.mideast:0.053\n","\tAverage Brier:0.048\n","\tAccuracy:0.902\n","\n","\n"],"name":"stdout"}]},{"metadata":{"id":"im1-YCMSf_-9","colab_type":"code","colab":{}},"cell_type":"code","source":["from sklearn.calibration import CalibratedClassifierCV\n","\n","name = [\"Multinomial\"\n","        ,\"Multinomial + Isotonic\"\n","        ,\"Multinomial + Sigmoid\"\n","        ,\"Complement\"\n","        ,\"Complement + Isotonic\"\n","        ,\"Complement + Sigmoid\"\n","        ,\"Bernoulli\"\n","        ,\"Bernoulli + Isotonic\"\n","        ,\"Bernoulli + Sigmoid\"]\n","\n","models = [MultinomialNB()\n","          ,CalibratedClassifierCV(MultinomialNB(), cv=2, method='isotonic')\n","          ,CalibratedClassifierCV(MultinomialNB(), cv=2, method='sigmoid')\n","          ,ComplementNB()\n","          ,CalibratedClassifierCV(ComplementNB(), cv=2, method='isotonic')\n","          ,CalibratedClassifierCV(ComplementNB(), cv=2, method='sigmoid')\n","          ,BernoulliNB()\n","          ,CalibratedClassifierCV(BernoulliNB(), cv=2, method='isotonic')\n","          ,CalibratedClassifierCV(BernoulliNB(), cv=2, method='sigmoid')\n","         ]"],"execution_count":0,"outputs":[]},{"metadata":{"id":"sBCWzmXnf_-9","colab_type":"code","outputId":"6e5a070d-54fd-4fbc-ee84-4df251ff627f","executionInfo":{"status":"ok","timestamp":1547616094613,"user_tz":-480,"elapsed":1388,"user":{"displayName":"Sen Yang","photoUrl":"","userId":"00832503676208839570"}},"colab":{"base_uri":"https://localhost:8080/","height":1394}},"cell_type":"code","source":["for name,clf in zip(name,models):\n","    clf.fit(Xtrain_,Ytrain)\n","    y_pred = clf.predict(Xtest_)\n","    proba = clf.predict_proba(Xtest_)\n","    score = clf.score(Xtest_,Ytest)\n","    print(name)\n","    Bscore = []\n","    for i in range(len(np.unique(Ytrain))):\n","        bs = BS(Ytest,proba[:,i],pos_label=i)\n","        Bscore.append(bs)\n","        print(\"\\tBrier under {}:{:.3f}\".format(train.target_names[i],bs))\n","    print(\"\\tAverage Brier:{:.3f}\".format(np.mean(Bscore)))\n","    print(\"\\tAccuracy:{:.3f}\".format(score))\n","    print(\"\\n\")"],"execution_count":74,"outputs":[{"output_type":"stream","text":["Multinomial\n","\tBrier under rec.sport.hockey:0.018\n","\tBrier under sci.space:0.033\n","\tBrier under talk.politics.guns:0.030\n","\tBrier under talk.politics.mideast:0.026\n","\tAverage Brier:0.027\n","\tAccuracy:0.975\n","\n","\n","Multinomial + Isotonic\n","\tBrier under rec.sport.hockey:0.006\n","\tBrier under sci.space:0.012\n","\tBrier under talk.politics.guns:0.013\n","\tBrier under talk.politics.mideast:0.009\n","\tAverage Brier:0.010\n","\tAccuracy:0.973\n","\n","\n","Multinomial + Sigmoid\n","\tBrier under rec.sport.hockey:0.006\n","\tBrier under sci.space:0.012\n","\tBrier under talk.politics.guns:0.013\n","\tBrier under talk.politics.mideast:0.009\n","\tAverage Brier:0.010\n","\tAccuracy:0.973\n","\n","\n","Complement\n","\tBrier under rec.sport.hockey:0.023\n","\tBrier under sci.space:0.039\n","\tBrier under talk.politics.guns:0.039\n","\tBrier under talk.politics.mideast:0.033\n","\tAverage Brier:0.033\n","\tAccuracy:0.986\n","\n","\n","Complement + Isotonic\n","\tBrier under rec.sport.hockey:0.004\n","\tBrier under sci.space:0.007\n","\tBrier under talk.politics.guns:0.009\n","\tBrier under talk.politics.mideast:0.006\n","\tAverage Brier:0.006\n","\tAccuracy:0.985\n","\n","\n","Complement + Sigmoid\n","\tBrier under rec.sport.hockey:0.004\n","\tBrier under sci.space:0.009\n","\tBrier under talk.politics.guns:0.010\n","\tBrier under talk.politics.mideast:0.007\n","\tAverage Brier:0.007\n","\tAccuracy:0.986\n","\n","\n","Bernoulli\n","\tBrier under rec.sport.hockey:0.068\n","\tBrier under sci.space:0.025\n","\tBrier under talk.politics.guns:0.045\n","\tBrier under talk.politics.mideast:0.053\n","\tAverage Brier:0.048\n","\tAccuracy:0.902\n","\n","\n","Bernoulli + Isotonic\n","\tBrier under rec.sport.hockey:0.016\n","\tBrier under sci.space:0.014\n","\tBrier under talk.politics.guns:0.034\n","\tBrier under talk.politics.mideast:0.033\n","\tAverage Brier:0.024\n","\tAccuracy:0.952\n","\n","\n","Bernoulli + Sigmoid\n","\tBrier under rec.sport.hockey:0.066\n","\tBrier under sci.space:0.030\n","\tBrier under talk.politics.guns:0.056\n","\tBrier under talk.politics.mideast:0.059\n","\tAverage Brier:0.053\n","\tAccuracy:0.879\n","\n","\n"],"name":"stdout"}]},{"metadata":{"id":"BkIqjsvhf_-_","colab_type":"code","colab":{}},"cell_type":"code","source":[""],"execution_count":0,"outputs":[]}]}